Flexible meta model (fmm) for an extensibility platform

ABSTRACT

According to embodiments of the disclosure, an example method herein may comprise: providing an extensibility platform for observing entities to produce associated observability data; installing flexible meta models within the extensibility platform, wherein the plurality of flexible meta models define the entities, a globally unique identifier of each of the plurality of entities, a type of each of the entities, relationships between the entities, kinds of observability data, and dependencies among the flexible meta models; and processing the observability data obtained within the extensibility platform based on tenant-specific solution packages and the flexible meta models, wherein the observability data is associated to the entities based on external references to corresponding globally unique identifiers of the entities, and wherein the observability data is sourced by sources configured to populate, based on a corresponding observed entity, attribute fields and tenant-specified tag fields according to that corresponding observed entity.

RELATED APPLICATION

This application claims priority to U.S. Prov. Appl. No. 63/325,910,filed Mar. 31, 2022, entitled FLEXIBLE META MODEL (FMM) FOR ANEXTENSIBILITY PLATFORM, by Bokhan-Dilawari, et al., the contents ofwhich are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to computer systems, and, moreparticularly, to a Flexible Meta Model (FMM) for an extensibilityplatform.

BACKGROUND

The Internet and the World Wide Web have enabled the proliferation ofweb services available for virtually all types of businesses and manyonline applications now rely on a distributed set of web services tofunction. These web services introduce complex data dependencies,complex data handling configurations, and various other operationalnuances, which make monitoring them particularly challenging. Indeed,the monitoring and logging of data across web services is currentlyhandled today in a discrete and/or non-centralized fashion with respectto each web service. Doing so in this manner also makes it difficult toassociate the logged data across the different web services. Inaddition, monitoring the web services in a discrete manner also runs therisk of breaking the software application already running in the cloud,such as when monitoring code is added for one web service withoutaccounting for where that web service fits within the overall executionof the application and with respect to its dependencies, data handling,etc.

This lack of relational awareness in monitoring the web servicesprevents referencing of dependencies across multiple software monitoringmodels, thereby preventing cross-model data observations.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIG. 1 illustrates an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example observability intelligence platform;

FIG. 4 illustrates an example of layers of full-stack observability;

FIG. 5 illustrates an example platform data flow;

FIG. 6 illustrates an example of a Flexible Meta Model (FMM);

FIGS. 7A-7B illustrate a high-level example of a container orchestrationdomain model;

FIG. 8 illustrates an example of a sophisticated subscription andlayering mechanism;

FIG. 9 illustrates an example interplay of tenant-specific solutionsubscription with cell management;

FIG. 10 illustrates an example of exposure of different configurationstores as a single API;

FIGS. 11A-11E illustrate an example of a common ingestion pipeline, inparticular where each of FIGS. 11A-11E illustrate respective portions ofthe pipeline;

FIG. 12 illustrates an example of resource mapping configurations;

FIG. 13 illustrates an example of a design of a Unified Query Engine(UQE);

FIG. 14 illustrates an example of a deployment structure of anobservability intelligence platform in accordance with the extensibilityplatform herein, and the associated cell-based architecture;

FIGS. 15A-15D illustrate an example of a system for utilizing aconfiguration-driven data processing pipeline for an extensibilityplatform, in particular where each of FIGS. 15A-15D illustraterespective quadrants of the system;

FIGS. 16A-16B illustrate a diagram representative of a Metrics, Events,Logs and Traces (MELT) data model herein;

FIG. 17 illustrates an example diagram of solution packaging accordingto the techniques herein;

FIG. 18 illustrates an example network of solution developers who areable to package solution configurations;

FIG. 19 illustrates an example of how the JSON store manages JSONobjects;

FIG. 20 illustrates an example architecture diagram for cell-based JSONstores;

FIGS. 21A-21E illustrate an example of layering within the JSON objectstore, in particular where FIG. 21A illustrates the entire example, andwhere FIGS. 21B-21E illustrate respective quadrants of the example;

FIG. 22 illustrates an alternative example of layering within the JSONobject store, in particular with regard to a user-global layerarrangement;

FIG. 23 illustrates an example of a logical model that defines therelation between various pieces of a JSON store;

FIG. 24 illustrates an example simplified procedure for implementing anextensibility platform;

FIG. 25 illustrates an example simplified procedure for utilizingFlexible Meta Model (FMM) for an extensibility platform; and

FIG. 26 illustrates an example simplified procedure for utilizingtenant-specific solution subscriptions for an extensibility platform, inaccordance with one or more embodiments described herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, an extensibilityplatform is introduced herein that allows for the monitoring of webservices in a centralized manner. The extensibility platform may beconfigured in part by one or more tenant-specific solution packages. Atenant may include one or more user or organization that utilizes asingle instance of an application. The extensibility platform alsoleverages a flexible meta model (FMM) that allow for the modeling ofMetrics, Events, Logs, and Traces (MELT) data through what is referredto herein as a “Flexible Metadata Modeling” FMM. The focus here is onthe interweaving of multiple models in a matrix form, where onedimension is the different artifact types (MELT, processing pipelineconfigs, user interface configurations, etc.) and the other dimension isthe domain (e.g. observability intelligence platforms, containerorchestration engines, end user monitoring, etc.). Interweaving refersto the fact that at any point in that matrix there can be safereferences to artifacts across both dimensions.

Specifically, according to one or more embodiments of the disclosure, anillustrative method herein may comprise: providing an extensibilityplatform for observing a plurality of entities to produce associatedobservability data, the extensibility platform configured in part by oneor more tenant-specific solution packages; installing a plurality offlexible meta models within the extensibility platform, wherein theplurality of flexible meta models define the plurality of entities, aglobally unique identifier of each of the plurality of entities, a typeof each of the plurality of entities, relationships between theplurality of entities, kinds of observability data, and dependenciesamong the plurality of flexible meta models; and processing theobservability data obtained within the extensibility platform based onthe one or more tenant-specific solution packages and the plurality offlexible meta models, wherein the observability data is associated tothe plurality of entities based on external references to correspondingglobally unique identifiers of the plurality of entities, and whereinthe observability data is sourced by a plurality of sources configuredto populate, based on a corresponding observed entity, one or moreattribute fields and one or more tenant-specified tag fields accordingto that corresponding observed entity

Other embodiments are described below, and this overview is not meant tolimit the scope of the present disclosure.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, and others.The Internet is an example of a WAN that connects disparate networksthroughout the world, providing global communication between nodes onvarious networks. Other types of networks, such as field area networks(FANs), neighborhood area networks (NANs), personal area networks(PANs), enterprise networks, etc. may also make up the components of anygiven computer network. In addition, a Mobile Ad-Hoc Network (MANET) isa kind of wireless ad-hoc network, which is generally considered aself-configuring network of mobile routers (and associated hosts)connected by wireless links, the union of which forms an arbitrarytopology.

FIG. 1 is a schematic block diagram of an example simplified computingsystem 100 illustratively comprising any number of client devices 102(e.g., a first through nth client device), one or more servers 104, andone or more databases 106, where the devices may be in communicationwith one another via any number of networks 110. The one or morenetworks 110 may include, as would be appreciated, any number ofspecialized networking devices such as routers, switches, access points,etc., interconnected via wired and/or wireless connections. For example,devices 102-104 and/or the intermediary devices in network(s) 110 maycommunicate wirelessly via links based on WiFi, cellular, infrared,radio, near-field communication, satellite, or the like. Other suchconnections may use hardwired links, e.g., Ethernet, fiber optic, etc.The nodes/devices typically communicate over the network by exchangingdiscrete frames or packets of data (packets 140) according to predefinedprotocols, such as the Transmission Control Protocol/Internet Protocol(TCP/IP) other suitable data structures, protocols, and/or signals. Inthis context, a protocol consists of a set of rules defining how thenodes interact with each other.

Client devices 102 may include any number of user devices or end pointdevices configured to interface with the techniques herein. For example,client devices 102 may include, but are not limited to, desktopcomputers, laptop computers, tablet devices, smart phones, wearabledevices (e.g., heads up devices, smart watches, etc.), set-top devices,smart televisions, Internet of Things (IoT) devices, autonomous devices,or any other form of computing device capable of participating withother devices via network(s) 110.

Notably, in some embodiments, servers 104 and/or databases 106,including any number of other suitable devices (e.g., firewalls,gateways, and so on) may be part of a cloud-based service. In suchcases, the servers and/or databases 106 may represent the cloud-baseddevice(s) that provide certain services described herein, and may bedistributed, localized (e.g., on the premise of an enterprise, or “onprem”), or any combination of suitable configurations, as will beunderstood in the art.

Those skilled in the art will also understand that any number of nodes,devices, links, etc. may be used in computing system 100, and that theview shown herein is for simplicity. Also, those skilled in the art willfurther understand that while the network is shown in a certainorientation, the system 100 is merely an example illustration that isnot meant to limit the disclosure.

Notably, web services can be used to provide communications betweenelectronic and/or computing devices over a network, such as theInternet. A web site is an example of a type of web service. A web siteis typically a set of related web pages that can be served from a webdomain. A web site can be hosted on a web server. A publicly accessibleweb site can generally be accessed via a network, such as the Internet.The publicly accessible collection of web sites is generally referred toas the World Wide Web (WWW).

Also, cloud computing generally refers to the use of computing resources(e.g., hardware and software) that are delivered as a service over anetwork (e.g., typically, the Internet). Cloud computing includes usingremote services to provide a user's data, software, and computation.

Moreover, distributed applications can generally be delivered usingcloud computing techniques. For example, distributed applications can beprovided using a cloud computing model, in which users are providedaccess to application software and databases over a network. The cloudproviders generally manage the infrastructure and platforms (e.g.,servers/appliances) on which the applications are executed. Varioustypes of distributed applications can be provided as a cloud service oras a Software as a Service (SaaS) over a network, such as the Internet.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the devices 102-106 shown in FIG. 1 above. Device 200 may compriseone or more network interfaces 210 (e.g., wired, wireless, etc.), atleast one processor 220, and a memory 240 interconnected by a system bus250, as well as a power supply 260 (e.g., battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over links coupled to thenetwork(s) 110. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Note, further, that device 200 may have multiple types ofnetwork connections via interfaces 210, e.g., wireless andwired/physical connections, and that the view herein is merely forillustration.

Depending on the type of device, other interfaces, such as input/output(I/O) interfaces 230, user interfaces (UIs), and so on, may also bepresent on the device. Input devices, in particular, may include analpha-numeric keypad (e.g., a keyboard) for inputting alpha-numeric andother information, a pointing device (e.g., a mouse, a trackball,stylus, or cursor direction keys), a touchscreen, a microphone, acamera, and so on. Additionally, output devices may include speakers,printers, particular network interfaces, monitors, etc.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise hardwareelements or hardware logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242, portions ofwhich are typically resident in memory 240 and executed by theprocessor, functionally organizes the device by, among other things,invoking operations in support of software processes and/or servicesexecuting on the device. These software processes and/or services maycomprise a one or more functional processes 246, and on certain devices,an illustrative “extensibility platform” process 248, as describedherein. Notably, functional processes 246, when executed by processor(s)220, cause each particular device 200 to perform the various functionscorresponding to the particular device's purpose and generalconfiguration. For example, a router would be configured to operate as arouter, a server would be configured to operate as a server, an accesspoint (or gateway) would be configured to operate as an access point (orgateway), a client device would be configured to operate as a clientdevice, and so on.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

——Observability Intelligence Platform——

As noted above, distributed applications can generally be deliveredusing cloud computing techniques. For example, distributed applicationscan be provided using a cloud computing model, in which users areprovided access to application software and databases over a network.The cloud providers generally manage the infrastructure and platforms(e.g., servers/appliances) on which the applications are executed.Various types of distributed applications can be provided as a cloudservice or as a software as a service (SaaS) over a network, such as theInternet. As an example, a distributed application can be implemented asa SaaS-based web service available via a web site that can be accessedvia the Internet. As another example, a distributed application can beimplemented using a cloud provider to deliver a cloud-based service.

Users typically access cloud-based/web-based services (e.g., distributedapplications accessible via the Internet) through a web browser, alight-weight desktop, and/or a mobile application (e.g., mobile app)while the enterprise software and user's data are typically stored onservers at a remote location. For example, using cloud-based/web-basedservices can allow enterprises to get their applications up and runningfaster, with improved manageability and less maintenance, and can enableenterprise IT to more rapidly adjust resources to meet fluctuating andunpredictable business demand. Thus, using cloud-based/web-basedservices can allow a business to reduce Information Technology (IT)operational costs by outsourcing hardware and software maintenance andsupport to the cloud provider.

However, a significant drawback of cloud-based/web-based services (e.g.,distributed applications and SaaS-based solutions available as webservices via web sites and/or using other cloud-based implementations ofdistributed applications) is that troubleshooting performance problemscan be very challenging and time consuming. For example, determiningwhether performance problems are the result of the cloud-based/web-basedservice provider, the customer's own internal IT network (e.g., thecustomer's enterprise IT network), a user's client device, and/orintermediate network providers between the user's client device/internalIT network and the cloud-based/web-based service provider of adistributed application and/or web site (e.g., in the Internet) canpresent significant technical challenges for detection of suchnetworking related performance problems and determining the locationsand/or root causes of such networking related performance problems.Additionally, determining whether performance problems are caused by thenetwork or an application itself, or portions of an application, orparticular services associated with an application, and so on, furthercomplicate the troubleshooting efforts.

Certain aspects of one or more embodiments herein may thus be based on(or otherwise relate to or utilize) an observability intelligenceplatform for network and/or application performance management. Forinstance, solutions are available that allow customers to monitornetworks and applications, whether the customers control such networksand applications, or merely use them, where visibility into suchresources may generally be based on a suite of “agents” or pieces ofsoftware that are installed in different locations in different networks(e.g., around the world).

Specifically, as discussed with respect to illustrative FIG. 3 below,performance within any networking environment may be monitored,specifically by monitoring applications and entities (e.g.,transactions, tiers, nodes, and machines) in the networking environmentusing agents installed at individual machines at the entities. As anexample, applications may be configured to run on one or more machines(e.g., a customer will typically run one or more nodes on a machine,where an application consists of one or more tiers, and a tier consistsof one or more nodes). The agents collect data associated with theapplications of interest and associated nodes and machines where theapplications are being operated. Examples of the collected data mayinclude performance data (e.g., metrics, metadata, etc.) and topologydata (e.g., indicating relationship information), among other configuredinformation. The agent-collected data may then be provided to one ormore servers or controllers to analyze the data.

Examples of different agents (in terms of location) may comprise cloudagents (e.g., deployed and maintained by the observability intelligenceplatform provider), enterprise agents (e.g., installed and operated in acustomer's network), and endpoint agents, which may be a differentversion of the previous agents that is installed on actual users' (e.g.,employees') devices (e.g., on their web browsers or otherwise). Otheragents may specifically be based on categorical configurations ofdifferent agent operations, such as language agents (e.g., Java agents,.Net agents, PHP agents, and others), machine agents (e.g.,infrastructure agents residing on the host and collecting informationregarding the machine which implements the host such as processor usage,memory usage, and other hardware information), and network agents (e.g.,to capture network information, such as data collected from a socket,etc.).

Each of the agents may then instrument (e.g., passively monitoractivities) and/or run tests (e.g., actively create events to monitor)from their respective devices, allowing a customer to customize from asuite of tests against different networks and applications or anyresource that they're interested in having visibility into, whether it'svisibility into that end point resource or anything in between, e.g.,how a device is specifically connected through a network to an endresource (e.g., full visibility at various layers), how a website isloading, how an application is performing, how a particular businesstransaction (or a particular type of business transaction) is beingeffected, and so on, whether for individual devices, a category ofdevices (e.g., type, location, capabilities, etc.), or any othersuitable embodiment of categorical classification.

FIG. 3 is a block diagram of an example observability intelligenceplatform 300 that can implement one or more aspects of the techniquesherein. The observability intelligence platform is a system thatmonitors and collects metrics of performance data for a network and/orapplication environment being monitored. At the simplest structure, theobservability intelligence platform includes one or more agents 310 andone or more servers/controllers 320. Agents may be installed on networkbrowsers, devices, servers, etc., and may be executed to monitor theassociated device and/or application, the operating system of a client,and any other application, API, or another component of the associateddevice and/or application, and to communicate with (e.g., report dataand/or metrics to) the controller(s) 320 as directed. Note that whileFIG. 3 shows four agents (e.g., Agent 1 through Agent 4) communicativelylinked to a single controller, the total number of agents andcontrollers can vary based on a number of factors including the numberof networks and/or applications monitored, how distributed the networkand/or application environment is, the level of monitoring desired, thetype of monitoring desired, the level of user experience desired, and soon.

For example, instrumenting an application with agents may allow acontroller to monitor performance of the application to determine suchthings as device metrics (e.g., type, configuration, resourceutilization, etc.), network browser navigation timing metrics, browsercookies, application calls and associated pathways and delays, otheraspects of code execution, etc. Moreover, if a customer uses agents torun tests, probe packets may be configured to be sent from agents totravel through the Internet, go through many different networks, and soon, such that the monitoring solution gathers all of the associated data(e.g., from returned packets, responses, and so on, or, particularly, alack thereof). Illustratively, different “active” tests may compriseHTTP tests (e.g., using curl to connect to a server and load the maindocument served at the target), Page Load tests (e.g., using a browserto load a full page—i.e., the main document along with all othercomponents that are included in the page), or Transaction tests (e.g.,same as a Page Load, but also performing multiple tasks/steps within thepage—e.g., load a shopping website, log in, search for an item, add itto the shopping cart, etc.).

The controller 320 is the central processing and administration serverfor the observability intelligence platform. The controller 320 mayserve a browser-based user interface (UI) 330 that is the primaryinterface for monitoring, analyzing, and troubleshooting the monitoredenvironment. Specifically, the controller 320 can receive data fromagents 310 (and/or other coordinator devices), associate portions ofdata (e.g., topology, business transaction end-to-end paths and/ormetrics, etc.), communicate with agents to configure collection of thedata (e.g., the instrumentation/tests to execute), and provideperformance data and reporting through the interface 330. The interface330 may be viewed as a web-based interface viewable by a client device340. In some implementations, a client device 340 can directlycommunicate with controller 320 to view an interface for monitoringdata. The controller 320 can include a visualization system 350 fordisplaying the reports and dashboards related to the disclosedtechnology. In some implementations, the visualization system 350 can beimplemented in a separate machine (e.g., a server) different from theone hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation,a controller instance 320 may be hosted remotely by a provider of theobservability intelligence platform 300. In an illustrative on-premises(On-Prem) implementation, a controller instance 320 may be installedlocally and self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents1-4) deployed to monitor networks, applications, databases and databaseservers, servers, and end user clients for the monitored environment.Any of the agents 310 can be implemented as different types of agentswith specific monitoring duties. For example, application agents may beinstalled on each server that hosts applications to be monitored.Instrumenting an agent adds an application agent into the runtimeprocess of the application.

Database agents, for example, may be software (e.g., a Java program)installed on a machine that has network access to the monitoreddatabases and the controller. Standalone machine agents, on the otherhand, may be standalone programs (e.g., standalone Java programs) thatcollect hardware-related performance statistics from the servers (orother suitable devices) in the monitored environment. The standalonemachine agents can be deployed on machines that host applicationservers, database servers, messaging servers, Web servers, etc.Furthermore, end user monitoring (EUM) may be performed using browseragents and mobile agents to provide performance information from thepoint of view of the client, such as a web browser or a mobile nativeapplication. Through EUM, web use, mobile use, or combinations thereof(e.g., by real users or synthetic agents) can be monitored based on themonitoring needs.

Note that monitoring through browser agents and mobile agents aregenerally unlike monitoring through application agents, database agents,and standalone machine agents that are on the server. In particular,browser agents may generally be embodied as small files using web-basedtechnologies, such as JavaScript agents injected into each instrumentedweb page (e.g., as close to the top as possible) as the web page isserved, and are configured to collect data. Once the web page hascompleted loading, the collected data may be bundled into a beacon andsent to an EUM process/cloud for processing and made ready for retrievalby the controller. Browser real user monitoring (Browser RUM) providesinsights into the performance of a web application from the point ofview of a real or synthetic end user. For example, Browser RUM candetermine how specific Ajax or iframe calls are slowing down page loadtime and how server performance impact end user experience in aggregateor in individual cases. A mobile agent, on the other hand, may be asmall piece of highly performant code that gets added to the source ofthe mobile application. Mobile RUM provides information on the nativemobile application (e.g., iOS or Android applications) as the end usersactually use the mobile application. Mobile RUM provides visibility intothe functioning of the mobile application itself and the mobileapplication's interaction with the network used and any server-sideapplications with which the mobile application communicates.

Note further that in certain embodiments, in the applicationintelligence model, a business transaction represents a particularservice provided by the monitored environment. For example, in ane-commerce application, particular real-world services can include auser logging in, searching for items, or adding items to the cart. In acontent portal, particular real-world services can include user requestsfor content such as sports, business, or entertainment news. In a stocktrading application, particular real-world services can includeoperations such as receiving a stock quote, buying, or selling stocks.

A business transaction, in particular, is a representation of theparticular service provided by the monitored environment that provides aview on performance data in the context of the various tiers thatparticipate in processing a particular request. That is, a businesstransaction, which may be identified by a unique business transactionidentification (ID), represents the end-to-end processing path used tofulfill a service request in the monitored environment (e.g., addingitems to a shopping cart, storing information in a database, purchasingan item online, etc.). Thus, a business transaction is a type ofuser-initiated action in the monitored environment defined by an entrypoint and a processing path across application servers, databases, andpotentially many other infrastructure components. Each instance of abusiness transaction is an execution of that transaction in response toa particular user request (e.g., a socket call, illustrativelyassociated with the TCP layer). A business transaction can be created bydetecting incoming requests at an entry point and tracking the activityassociated with request at the originating tier and across distributedcomponents in the application environment (e.g., associating thebusiness transaction with a 4-tuple of a source IP address, source port,destination IP address, and destination port). A flow map can begenerated for a business transaction that shows the touch points for thebusiness transaction in the application environment. In one embodiment,a specific tag may be added to packets by application specific agentsfor identifying business transactions (e.g., a custom header fieldattached to a hypertext transfer protocol (HTTP) payload by anapplication agent, or by a network agent when an application makes aremote socket call), such that packets can be examined by network agentsto identify the business transaction identifier (ID) (e.g., a GloballyUnique Identifier (GUID) or Universally Unique Identifier (UUID)).Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransactions can provide information on whether a service is available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

In accordance with certain embodiments, the observability intelligenceplatform may use both self-learned baselines and configurable thresholdsto help identify network and/or application issues. A complexdistributed application, for example, has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed observability intelligence platform canperform anomaly detection based on dynamic baselines or thresholds, suchas through various machine learning techniques, as may be appreciated bythose skilled in the art. For example, the illustrative observabilityintelligence platform herein may automatically calculate dynamicbaselines for the monitored metrics, defining what is “normal” for eachmetric based on actual usage. The observability intelligence platformmay then use these baselines to identify subsequent metrics whose valuesfall out of this normal range.

In general, data/metrics collected relate to the topology and/or overallperformance of the network and/or application (or business transaction)or associated infrastructure, such as, e.g., load, average responsetime, error rate, percentage CPU busy, percentage of memory used, etc.The controller UI can thus be used to view all of the data/metrics thatthe agents report to the controller, as topologies, heatmaps, graphs,lists, and so on. Illustratively, data/metrics can be accessedprogrammatically using a Representational State Transfer (REST) API(e.g., that returns either the JavaScript Object Notation (JSON) or theeXtensible Markup Language (XML) format). Also, the REST API can be usedto query and manipulate the overall observability environment.

Those skilled in the art will appreciate that other configurations ofobservability intelligence may be used in accordance with certainaspects of the techniques herein, and that other types of agents,instrumentations, tests, controllers, and so on may be used to collectdata and/or metrics of the network(s) and/or application(s) herein.Also, while the description illustrates certain configurations,communication links, network devices, and so on, it is expresslycontemplated that various processes may be embodied across multipledevices, on different devices, utilizing additional devices, and so on,and the views shown herein are merely simplified examples that are notmeant to be limiting to the scope of the present disclosure.

——An Extensibility Platform——

One specific example of an observability intelligence platform above isthe AppDynamics Observability Cloud (OC), available from Cisco Systems,Inc. of San Jose, California. The AppDynamics OC is a cloud-nativeplatform for collecting, ingesting, processing and analyzing large-scaledata from instrumented complex systems, such as Cloud system landscapes.The purpose of the platform is to host solutions that help customers tokeep track of the operational health and performance of the systems theyobserve and perform detailed analyses of problems or performance issues.

AppDynamics OC is designed to offer full-stack Observability, that is,to cover multiple layers of processes ranging from low-level technicalprocesses such as networking and computing infrastructure overinter-service communication up to interactions of users with the systemand business processes, and most importantly, the interdependenciesbetween them. FIG. 4 , for example, illustrates an example 400 of layersof full-stack observability, demonstrating measurable softwaretechnologies, sorted and grouped by proximity to the end customer. Forinstance, the layers 410 and associated technologies 420 may be suchthings as:

-   -   Outcomes:        -   payment/revenue; goods/services received; inventory updated;            dissatisfaction/satisfaction; success/failure; support;            brand capital; etc.    -   Interactions:        -   page views; impressions; gestures; clicks; voice commands;            keystrokes; downloads; attention; etc.    -   Experiences:        -   sessions; app usage; IoT usage; messaging/notifications;            waiting/latency; errors/bugs etc.    -   Journeys:        -   business journeys; workflows; etc.    -   App Flows:        -   business transactions; service endpoints; calls; third party            “backends”; etc.    -   Applications:        -   application services; APIs; microservices; scripts; daemons;            deployments; etc.    -   Infrastructure Services:        -   databases; virtual machines; containers; orchestration;            meshes; security services; logging; etc.    -   Infrastructure:        -   servers; networks; storage; compute; datacenters; load            balancers; etc.

Each of these layers has different types of entities and metrics thatneed to be tracked. Additionally, different industries or customers mayhave different flavors of each layer or different layers altogether. Theentirety of artifacts represented in each layer and their relationshipscan be described—independent of any digital representation—in a domainmodel.

In the development of a conventional application, the domain model isencoded in a data model which is pervasively reflected in the coding ofall parts of a solution and thus predetermines all its capabilities. Anysubstantial extension of these capabilities requiring changes in thedata model results in a full iteration of the software lifecycle,usually involving: Updating database schemas, data access objects,in-memory representation of data, data-processing algorithms,application interface (API), and user interface. The coordination of allthese changes to ensure the integrity of the solution(s) is particularlydifficult in cloud-native systems due to their distributed nature, andsubstantial teams in every software company are dedicated to this task.

The task becomes harder the more moving parts and the more actors areinvolved. But the sheer bandwidth of domain models and functionalityhinted at in FIG. 4 above makes it all but impossible for a singlecompany to deliver all the required solutions in a centralizeddevelopment process. A platform thus should allow customers and partnersto adapt and extend the solutions, or even provide entirely newsolutions, with minimal risk of breaking or compromising the productionsystem running in the cloud. The biggest challenge lies in the fact thatall these solutions are not isolated from each other but must run foreach tenant as an individually composed, integrated application sharingmost of the data and infrastructure.

In order to make this possible, the techniques herein are directed attaking a novel approach to solution composition, informed by elements ofmodel-driven architecture, graph data models, and modern pull-basedsoftware lifecycle management. That is, the techniques herein,therefore, are directed toward an extensibility platform that provides asolution packaging system that allows for data-type dependencies.

Operationally, the extensibility platform is built on the principle ofstrictly separating the solutions from the executing platform'stechnology stack in order to decouple their respective life cycles. Thesolutions are very much (e.g., almost entirely) model-driven, so thatthe platform can evolve and undergo optimizations and technologicalevolution without affecting the existing solutions. In the rare cases inwhich the models are not powerful enough, custom logic can be providedas a Function as a Service (FaaS) or container image exposing awell-defined service interface and running in a strictly controlledsandbox. FIG. 5 , for instance, showing a platform data flow 500(described further below), illustrates how different solution-specificartifacts 510 interact with the platform's core functionality 520 (e.g.,the data flow in the middle).

Solutions herein thus provide artifacts that enrich, customize, or alterthe behavior data ingestions, processing, and visualizations. Thisallows a company and/or application such as IT management companies/appsto provide a customized monitoring solution for data managementplatforms (e.g., NoSQL databases), for example, on the observabilityintelligence platform above. Such a custom solution may thereforeinclude the definition of data management platform entities that aremonitored, and the relationship between those entities, and theirmetrics. The example IT management app for data management platforms canalso provide enrichments to the user interface, such as providingdistinct iconography for their entities, and bundling dashboards andalerts that take particular advantage of data managementplatform-specific metrics, such as a data management platform heartbeatmetric. This same system of packaging may be used to provision thesystem with having “core” domains specific to the illustrativeobservability intelligence platform, the only difference being thatsubscription to system apps is automatic. In addition, first party appslike EUM may also leverage the same system.

In particular, the extensibility platform techniques herein are directedto a solution packaging system that allows for data-type dependencies.It is essentially the JSON store and solution packaging that arecollectively referred to herein as “Orion”. The system is designed toallow modules to have dependencies like a traditional code/packagingsystem like java+maven, while simultaneously allowing these models todefine their data model, access to that data model, packaging of objectsconforming to other data solution data models, etc. This relies heavilyon the concept of “layering”, which is described further below ingreater detail. While other systems may allow layering of local files,the ability to have layers that include global dynamic layers, as wellas static global layers provided as part of a solution is never beforeseen, and solves a big problem.

As described herein, the techniques herein provide a system designed toprovide “full stack observability” for distributed computer systems.That is, the system provides the ability to receive Metrics, Events,Logs, and Traces (MELT) data/signals in accordance with Open Telemetrystandards. It also provides the ability to maintain an internal model ofthe actual entities being observed, as well as an ability to mapincoming data/signals to entities under observation. Further, theextensibility platform herein provides the ability to query the entitiesof the system with regard to their associated MELT data/signals, and toinfer health and other computed signals about entities. Entities mayalso be grouped together into composite entities to thus receive,generate, and maintain data/signals about composite entities,accordingly. Moreover, as detailed herein, the platform also has anopenness to first, second, and third parties to “extend” all of theabove so that the platform can continuously incorporate new use caseswithout each use case having to be “hand written” by the coreengineering team.

The techniques herein also provide extensibility in a multi-tenant,app-aware, platform for MELT data processing, allowing for third partiesto create solutions to which tenants can subscribe, and allowing forsystem capabilities to be defined and packaged in a way that isfunctionally identical to third party solutions. In addition, thisallows third parties to extend the platform with capabilities notpreviously envisioned, such as, e.g., to augment the platform with newdata types and storage for instances of those types, to augment theplatform with new functions (lambda style), to augment the platforminterfaces (REST, gRPC) with new APIs whose implementation is backed bylambda style functions and data storage, to augment the platform'sbuilt-in data processing in ways that benefit the solution withoutimpacting tenants who have not subscribed to the solution, and so on.

Through providing extensibility in a multi-tenant, app-aware, platformfor MELT data processing, the techniques herein also provide anextensible object modeling system for a multi-tenant microservicesarchitecture. This allows dynamic composition of objects from mutablelayers, which allows for applications/solutions to define object types,and for applications/solutions to bundle object instances (instances maybe of a type defined by another solution that is a dependency or definedlocally in the same solution). It also allows for tenants to overrideapplication/solution values, which enables tenants to customize thebehavior of a solution.

The dynamic composition of objects from mutable layers also allows animplementation comprised of a tree-shaped object layering system withlayers/awareness for, illustratively:

-   -   depth 0 (tree root): global system settings/fields;    -   depth 1: global application/solution constructs;    -   depth 2: account (a collection of tenants spanning multiple        cells);    -   depth 3: tenant; and    -   depth 4: user.

Moreover, the dynamic composition of objects from mutable layers furtherallows a communication system between globally distributed cells toenable each cell to have a synchronized local copy of the global layers,as well as a read-time composition system to compose object from layers.

The extensible object modeling system for a multi-tenant microservicesarchitecture further provides a system for global solution management,which comprises a method of packaging apps/solutions, a method ofdeclaring dependencies between solutions, a customer facing solutionregistry allowing developers to list their solutions, and so on.

The multi-tenant microservices architecture further provides a typesystem of meta-data for defining objects and their layers. That is, thetechniques herein allow for specifying the shape of objects, declaringglobal/solution level object instances inside of solution packages,specifying which fields of the object support layering, specifying whichfields are secrets, allowing inter-object references (e.g., allowingruntime spreading of fields to support inheritance and other use cases,allowing recursive prefetching of fields, allowing references to globalobject-layer-resident instances, etc.), and so on.

Additionally, the multi-tenant microservices architecture hereinprovides a system for managing object storage and retrieval by type. Forinstance, such a system may define a method of routing traffic to objectstores based on the object type (e.g., a federation of object storesproviding a single API/facade to access all types), as well as allowingatomic, eventually consistent maintenance of references between objects.

The extensible object modeling system for a multi-tenant microservicesarchitecture additionally provides a system for ensuring atomicity ofinstallation and updates to multi-object application/solutions acrossmicroservices in a cell. It also provides a library/client that allowspieces of our internal system to query and observe objects for changes(e.g., allowing MELT data ingestion pipeline to store configurationobjects in memory, and avoiding having to query for “freshness” eachtime the object is needed).

As detailed herein, there are numerous concepts generally addressed bythe extensibility platform of the present disclosure. Such concepts maycomprise such things as:

-   -   a programmable data ingestion framework;    -   atomic maintenance of references between objects in a        distributed type system;    -   atomicity of keys in document shredding for domain events;    -   automation of sagas in a distributed object store;    -   type systems in functions as a service (FaaS);    -   large scale data collection programmable by an end user;    -   managing multi-tenancy in data ingestion pipeline;    -   federation of a distributed object store;    -   improvements to operations in a distributed object store;    -   expression of user interface customization in terms of flexibly        defined entity models;    -   a system of type layering in a multitenant, global distributed        system;    -   customizing the inputs of a multi-tenant distributed system;    -   management of secure keys in a distributed multi-tenant system;    -   managing secure connections to external systems in a “bring your        infrastructure” scenario;    -   automating workflows for the collection of secrets in a layered        configuration system;    -   protecting developer secrets in FaaS environment;    -   Optimization of FaaS using intelligent caching in a programmable        distributed data environment;    -   automating failover and restoration in a cell based        architecture;    -   a modular entity modeling system;    -   a potential replacement for traditional telemetry for        dashboards;    -   eventually consistent deployment of artifacts in distributed        data processing pipeline;    -   Configuration-driven extensible MELT data processing pipeline;    -   Extracting additional value from the MELT data via customizable        workflows;    -   Creating a graph-centric model from MELT data for observability;    -   Tag-aware attribute based access control for distributed        systems;    -   Metadata-based graph schema definition;    -   Ensuring fairness in a multi-tenant system via rate limiting;    -   Configuration-driven Query Composition for Graph Data        Structures;    -   And so on.

Notably, and to aide in the discussion below, the smallest deployableunit of extension is a “solution”, which is a package of models,configurations, and potentially container images for customizingextension points. Solutions can depend on other solutions. For example,a system health solution depends on a “Flexible Meta Model” (FMM)solution (described below), since health apps provide entities andmetrics that depend on an FMM-type system. Core solutions may beautomatically installed in each cell (e.g., similar to how certainplatforms come with certain libs pre-installed with the system). Notefurther that a “solution artifact” is a JSON configuration file that asolution uses to configure an extension point.

An extension point, that is, is a part of the extensibility platformthat is prepared to accept a configuration or other artifact to steerits behavior. Since the architecture of the extensibility platformherein is largely model-driven, most of the extensions can be realizedby means of soft-coded artifacts: Model extensions and configurationsexpressed as JSON or other declarative formats. For instance, as shownin the extensibility platform data flow 500 in FIG. 5 , soft-codedextension artifacts 512 are shown, while for more complex—orstateful—logic, services can be plugged in, i.e., custom containerimages 514. The extension points can be divided into four groups, Model,Pre-Ingestion, Processing, and Consumption, as shown:

-   -   Model 530 (e.g., entity types 532, association types 534, and        metric types 536);    -   Pre-Ingestion 540 (e.g., collection configuration 542, agent        configuration 544, and pre-ingestion transformations 546);    -   Processing 550 (e.g., mapping rules 552, and processing rules        554); and    -   Consumption. 560 (e.g., UI configuration 562, report        configuration 564, and webhook configuration 566)        Moreover, custom container images 514 may comprise such things        as a Cloud Collector 572 and Custom Logic 574.

As also shown in FIG. 5 , the platform's core functionality 520 maycomprise collection 582, pre-ingestion 584 (e.g., with agentconfiguration 544 coming via an observability or “AppD” agent 586),ingestion 588, processing 590, MELT store 592, and an FMM 594, with thefunctionalities being interconnected to each other and/or to thedifferent solution-specific artifacts 510 as shown, and as generallydescribed in detail herein.

Regarding details of the extensibility platform of the presentdisclosure, at the core of the extensibility platform herein is theFlexible Meta Model (FMM), which allows creation of models of eachsolution's specific artifacts, that is, entities (such as services oruser journeys) and their associated observed data: Metrics, Events, Logsand Traces (together abbreviated as MELT).

FIG. 6 shows a simplified schematic of the FMM 600. Each of the shadedboxes represents a “kind” of data 605 for which specific types (andinstances) can be defined. Entity types 610 may have a property 612,fact 614, and tag 616. Examples for entity types 610 are: Service,Service Instance, Business Transaction, Host, etc.

Relationship types 620 define how entities are associated to each other(for example “contains” or “is part of”). Interaction types 630 describehow entities interact with each other. They combine the semantics ofassociation types (e.g., a service “calls” a backend) with thecapability of entity types to declare MELT data (Metric 642, Event 644,Log Record 646, and Trace 648 (with Span 649). In one embodiment,interaction types are treated just like entity types, though not so inother embodiments.

Based on this meta model, models of specific domains (such as acontainer orchestration) can be created. For instance, FIGS. 7A-7Billustrates a high-level example of a container orchestration domainmodel 700 (e.g., a Kubernetes or “K8s” domain model). The containerorchestration domain model 700 may be made up of model components 702(e.g., 702-1 . . . 702-N) organized with the illustrated relationships(e.g., subtype, one-to-many relationship, many-to-many relationship,one-to-one relationship). Additionally, the container orchestrationdomain model 700 may include model components that are external domainmodel components 704 (e.g., 704-1 . . . 704-N) that represent externaldomains sharing the illustrated relationships to the other modelcomponents 702. These models determine the content that a usereventually sees on their screen.

The description below provides greater details regarding the FlexibleMeta Model (FMM) for an Extensibility Platform.

To complement this flexible metamodel, the platform has schema-flexiblestores to hold the actual data: The graph-based entity store andschema-flexible stores for metrics, events, logs and tracesrespectively. Thus, a customer who wants to extend the data model justmodifies the corresponding model in the FMM and can immediately startpopulating the data stores with the respective data, without having tomake changes to the data stores themselves.

Corresponding changes in the models/configurations driving the dataprocessing pipeline will immediately start generating the data topopulate the stores according to the model changes. An important featureof the extensibility platform is that it doesn't treat the respectivemodels of a solution (FMM data model, data processing and consumptionmodels) in isolation. These models refer to each other (e.g., a UI fieldwill have a reference to the field in the data model it represents) andthe integrity and consistency of these mutual references is tracked andenforced.

The description below also provides greater details regarding theseconcepts within the FMM.

Notably, the description below provides greater details regardingTenant-Specific Solution Subscriptions.

The extensibility platform herein is cloud-native, but at the same time,it allows every tenant to experience it as an individually configuredapplication that reflects their specific business and angle of view. Thetenants achieve this by selectively subscribing to solutions for eachaspect of their business, and in some cases by even adding their owncustom solutions.

This is made possible by a sophisticated subscription and layeringmechanism, illustrated in FIG. 8 , illustrating tenant-specific behaviorof the extensibility platform as a result of selective activation andlayering of models. In this example mechanism 800, the solution registry810 has three registered solutions, the platform core 812, End UserMonitoring (EUM) 814 and a hypothetical third party solution, such asManageEngine for MongoDB 816. Each of these solutions contains modelsfor cloud connections and custom endpoints 822, MELT data ingestion andprocessing 824, and User Interfaces 826, respectively.

For each tenant (e.g., “A” or “B”), only the models that they aresubscribed to are being used in the course of data collection,ingestion, processing and consumption, hence the experience of thetenant A user 832 in FIG. 8 is different from that of the tenant B user834.

A particularly noteworthy characteristic of the platform herein is thatthese solutions don't necessarily live side-by-side. Rather, a solutioncan build on top of another solution, amend, and customize it. The finalexperience of tenant A user is therefore the result of the layering ofthe three subscribed solutions, where each can make modifications of themodels of the layers below.

Notably, the scaling model of the extensibility platform herein is basedon cells, where each cell serves a fixed set of tenants. Thus, thesolution registry and model stores of each cell keep the superset of allthe solutions (and the corresponding artifacts) to which the tenants ofthe cell have subscribed. When a tenant subscribes to a solution, thesolution registry checks whether that solution is already present in thecell. If not, it initiates a pull from the solution repository.

This concept is shown generally in FIG. 9 , illustrating an exampleinterplay 900 of tenant-specific solution subscription with cellmanagement. In particular, tenants 910 exist within a cell 920, with anassociated container orchestration engine 930 which pulls solutions 945from a solution repository 940 (“solution repo”). A user interface 950for the extensibility platform, such as an observability intelligenceplatform, can then illustrate an enhanced experience with customsolutions, accordingly.

Notably, in FIG. 9 , when a solution is present in the cell (i.e., allits artifacts are present in the corresponding model stores), thesolution is activated for the tenant. At that moment, the correspondingmodels/configurations will start taking effect.

Since the extensibility platform herein is a large distributed system,the models and configurations are not centrally stored but rather inmultiple stores, each associated with one or more consumers of therespective model. Each of these stores is an instance of the samegeneric JSON store, and through routing rules, they are exposed as asingle API with consistent behavior.

FIG. 10 illustrates an example 1000 of exposure of the differentconfiguration stores as a single API. In particular, as shown, the JSONstore appears as a single API and illustratively begins at service meshrouting rules 1010, where requests may be path-routed to the right storebased on the <type> part of the REST path. The example stores maycomprise dashboards 1022, FMM 1024, UI preferences 1026, custom stores1028 (e.g., “Your Team's Domain Here”), and so on. From there, each“type table” lives in exactly one store. For instance, dashboard table1032 (from dashboards 1022), FMM schema table 1034 or FMM config table1035 (e.g., depending upon the access into FMM 1024), UI preferencesconfig table 1036 from UI prefs 1026, and custom tables 1038 (e.g., fromcustom stores 1028, such as “Your Team's object type” from “Your Team'sDomain Here”).

Notably, the description below also provides greater details regardingthe JSON object store.

Regarding a configuration-driven data processing pipeline herein, a corefeature of the extensibility platform herein is its ability to ingest,transform, enrich, and store large amounts of observed data from agentsand OpenTelemetry (OT) sources. The raw data at the beginning of theingestion process adheres to the OpenTelemetry format, but is doesn'thave explicit semantics. In a very simplified way, the raw data can becharacterized as trees of key-value pairs and unstructured text (in thecase of logs).

The purpose of the processing pipeline is to extract the meaning of thatraw data, to derive secondary information, detect problems andindicators of system health, and make all that information “queryable”at scale. An important part of being queryable is the connection betweenthe data and its meaning, i.e., the semantics, which have been modeledin the respective domain models. Hence the transformation from raw datato meaningful content can't be hard-coded, it should (e.g., must) beencoded in rules and configurations, which should (e.g., must) beconsistent with the model of each domain.

FIGS. 11A-11E illustrate an example of a common ingestion pipeline,e.g., the whole ingestion and transformation process. For claritypurposes, FIGS. 11A-11E each illustrate a respective portion of theentire pipeline. For example, FIGS. 11A-11B collectively illustrate afirst quadrant 1100 a including an ingestion portion 1106 of thepipeline, FIG. 11C illustrates a second quadrant 1100 b including apersistence 1108 portion of the pipeline, FIG. 11D illustrates a thirdquadrant 1100 c including a post-ingestion portion 1110 of the pipeline,and FIG. 11E illustrates a fourth quadrant 1100 d including a secondpost ingestion portion 1112 and a metadata portion 1114 of the pipeline.Each of the quadrants may include transformation steps. Thesetransformation steps may take the form of services 1102 (e.g., 1102-1 .. . 1102-N) or of applications 1116 (e.g., 1116-1 . . . 1116-N) whichmay include a collection of related services. Each of the quadrants mayalso include data queues 1104 (e.g., 1104-1 . . . 1104-N) (e.g., Kafkatopics) that the steps subscribe to and feed into. Steps with a cogwheelsymbol 1120 (e.g., 1120-1 . . . 1120-N) may be controlled byconfiguration objects, which means that they can be configurableextensibility taps adaptable to new domain models by the mere additionor modification of configurations. Steps with a plug symbol 1122 mayinclude pluggable extensibility taps.

For example, the first quadrant 1100 a may include common ingestionservice 1102-1 (e.g., associated with rate limiting, licenseenforcement, and static validation), resource mapping service 1102-2(e.g., associated with mapping resources to entities, adding entitymetadata, resource_mapping, entity_priority, etc.), metric mappingservice 1102-3 (e.g., associated with mapping and transforming OTmetrics to FMM, metric mapping, etc.), log parser service 1102-4 (e.g.,associated with parsing and transforming logs into FMM events, etc.),span grouping service 1102-5 (e.g., associated with grouping spans intotraces within a specified time window, etc.), trace processing service1102-6 (e.g., associated with deriving entities from traces andenriching the spans, etc.), and/or tag enrichment service 1102-7 ((e.g.,associated with adding entity tags to MELT data and entities,enrichment, etc.).

In addition, this quadrant may include datalct.ot-raw-metrics.v1 dataqueue 1104-1, datalct.ot-raw-logs.v1 data queue 1104-2,data.fct.ot-raw-spans.v1 data queue 1104-3, data.sys.raw-metrics.v1 dataqueue 1104-5, data.sys.raw-logs.v1 data queue 1104-6,data.sys.raw-spans.v1 data queue 1104-7, data.fct.raw-metrics.v1 dataqueue 1104-8, data.fact.raw-events.v1 data queue 1104-9,data.fct.raw-logs.v1 data queue 1104-10, datalct.raw-traces.v1 dataqueue 1104-11, data.fct.processed-traces.v1 data queue 1104-12,datalct.raw-topology.v1 data queue 1104-13, datalct.metrics.v1 dataqueue 1104-14, datalct.events.v1 data queue 1104-15, datalct.logs.v1data queue 1104-16, data.fct.traces.v1 data queue 1104-17, and/ordata.fct.topology.v1 data queue 1104-18.

The second quadrant 1100 b may include metric writer application 1116-1(e.g., associated with writing metrics to the metric store 1118-1 (e.g.,druid)), event writer application 1116-2 (e.g., associated with writingevents to the event store 1118-2 (e.g., dashbase)), trace writerapplication 1116-3 (e.g., associated with writing sampled traces to thetrace store 1118-3 (e.g., druid)), and/or topology writer 1116-N (e.g.,associated with writing entities and associations to the topology store1118-4 (e.g., Neo4J). Additionally, this quadrant may includesystemIct.events.v1 data queue 1104-N.

The third quadrant 1100 c may include topology metric aggregationservice 1102-8 (e.g., associated with aggregating metrics based onentity relationships, etc.), topology aggregation mapper service 1102-9(e.g., associated with aggregating metrics, mertic_aggregation, etc.),raw measurement aggregation service 1102-10 (e.g., associated withconverting raw measurements into metrics, etc.), metric derivationservice 1102-11 (e.g., associated with deriving measurements from meltdata, metric_derivations, etc.), and/or sub-minute metric aggregationservice 1102-12 (e.g., associated with aggregating sub-minute metricsinto a minute, etc.). Additionally, this quadrant may includedata.sys.pre-aggregated-metrics.v1 data queue 1104-19,data.fct.raw-measurements.v1 data queue 1104-20, and/ordata.fct.minute-metrics.v1 data queue 1104-21.

The fourth quadrant 1100 d may include topology derivation service1102-13 (e.g., associated with deriving additional topology elements,entity_grouping, relationship_derviation, etc.), all configurationservices 1102-14, schema service 1102 (e.g., associated with managingFMM types), and/or MELT config service 1102-N (e.g., associated withmanaging MELT configurations, etc.). In addition, this quadrant mayinclude schema store 1118-5 (e.g., couchbase) and/or MELT config store1118-N (e.g., couchbase).

Other components and interconnections/relationships may be made in acommon ingestion pipeline architecture. The views and productsillustrated in FIG. 11A-11E are shown herein merely as exampleimplementations that may be used to provide and/or support one or morefeatures of the techniques herein.

A typical example of rule-driven transformation is the mapping of theOpen Telemetry Resource descriptor to an entity in the domain model. TheResource descriptor contains key-value pairs representing metadata aboutthe instrumented resource (e.g., a service) that a set of observed data(e.g., metrics) refers to. The task of the Resource Mapping Service isto identify the entity, which the Resource descriptor describes, and tocreate it in the Topology Store (which stores entities and theirrelations) if it isn't known yet.

FIG. 12 illustrates an example of resource mapping configurations 1200.In particular, the three specific examples for a resource mappingconfiguration are, essentially:

-   -   1210: For service instances, copy all matching attribute names        to properties and remaining to tags (match by convention);    -   1220: Copy all attributes starting with “service.” to entity        properties—copy remaining to tags;    -   1230: Define specific mappings for entity attribute and tags.

As shown in FIG. 12 , an expression “scopeFilter” is used to recognizethe input (i.e., records not matching the scope filter are ignored) and“fmmType” assigns an entity type to the resource if it is recognized.The mappings rules then populate the fields of the entity (as declaredin the domain model) with content derived from the OpenTelemetrycontent. Thus the resource mapping configuration refers to, andcomplements, the domain model, enabling individual tenants to observeand analyze the respective entities in their own system landscaperegardless of whether the extensibility platform (e.g., theobservability intelligence platform above) supports these entity typesas part of the preconfigured (“out of the box”) domain models.

The totality of these models and configurations can be considered as onecomposite multi-level model. Composite in the sense that it has partscoming from different organizations (e.g., the observabilityintelligence platform distributor, customers, third parties, etc.) andmulti-level in the sense that the artifacts drive the behavior ofdifferent parts of the whole system, e.g., ingestion, storage, UserInterface, etc. Since artifacts refer to each other both across originand across technical level, the reliable operation of the system heavilyrelies on the JSON store's ability to understand and enforce theconsistency of these references.

For the Trace Processing Service, even more flexibility is required.What is shown as a single box in the diagram is actually itself aworkflow of multiple processing steps that need to be dynamicallyorchestrated depending on the respective domain.

The description below provides greater details regarding theConfiguration-Driven Data Processing Pipeline.

Regarding embedding custom container images and FaaS, in accordance withthe techniques herein, especially in the complex trace processingworkflows, but also in pre-ingestion processing (such as the enrichmentof observed data with geographic information derived from IP addresses),some required transformations are too sophisticated for genericrule-driven algorithms. In such cases, the customer must be able toprovide their logic as a function that can be executed as a service(e.g., a FaaS) or even a container image exposing a well-defined serviceinterface.

Note that where custom functions are running external to theextensibility platform, the corresponding secrets to access them need tobe made available to calling services.

Another security-related problem coming with custom services is thattheir access may need to be restricted based on user roles. One solutionto this is to use custom representational state transfer (REST)endpoints and extensible role-based access control (RBAC) for anextensibility platform.

The extensibility platform herein also illustratively uses a graph-basedquery engine. In particular, an important precondition for theconfiguration-driven consumption of customer-specific content is theability to query data via a central query engine exposing a graph-basedquery language (as opposed to accessing data via multiple specificservices with narrow service interfaces).

FIG. 13 illustrates an example of a design of a Unified Query Engine(UQE) 1300. The Unified Query Engine 1300, in particular, providescombined access to:

-   -   Topology (Entities and their relationships);    -   Metrics;    -   Events;    -   Logs; and    -   Traces.        The Unified Query Engine 1300 may provide the combined access by        receiving a fetch request 1302, performing compilation 1304 and        determining execution plan 1306. In addition, Unified Query        Engine 1300 may execution 1310 and response 1312. Results of        performing compilation 1304 and/or execution plan 1306 may be        cached with schema service 1305. Results of execution 1310 may        be stored in observability stores 1311 which may include a        metric store, a topology store, a DashBase store, a trace store,        etc. For example, the topology data may be stored in a graph        database, and the unified query language (UQL) may allow the        platform to identify sets of entities and then retrieve related        data (MELT) as well as related entities. The ability to traverse        relationships to find related entities enables the application        of graph processing methods to the combined data (entities and        MELT).

The extensibility platform herein also uses a Configuration-Driven UserInterface. In order to allow customers and third parties to createdomain-specific UIs without deploying code, the UI is built according tothe following principles:

1. No domain knowledge is hard-coded into any UI components.

-   -   In particular, no references whatsoever to FMM model content        occur in the UI code.        2. Domain knowledge is modeled into UI configurations.    -   The appearance of the UI, as far as it is domain-specific, is        determined by declarative configurations for a number of        predefined building blocks.        3. Uniform modeling approach, reusable configurations.    -   Regardless of the page context (Dashboard, Object Centric Pages        (OCP), etc.), the same things are always configured in the same        way. Existing configurations can be reused in different        contexts. Reusable configurations declare the type of entity        data they visualize, and reuse involves binding this data to a        parent context.        4. Dynamic selection of configurations.    -   On all levels, configurations can be dynamically selected from        multiple alternatives based on the type (and subtype) of the        data/entity to which they are bound. The most prominent example        is the OCP template, which is selected based on the type of the        focus entity (or entities).        5. Nesting of configurable components, declarative data binding.    -   Some components can be configured to embed other components. The        configurations of these components declare the binding of their        child components to data related to their own input. No        extension-specific hard-coded logic is required to provide these        components with data. This gives third parties enough degrees of        freedom to create complex custom visualizations.

6. Limited Interaction Model.

-   -   In contrast to the visualization, third parties have limited        ways to influence the behavior of the application. The general        Human Computer Interaction mechanics remain the same for all        applications. For example, it is possible to select the        “onclick” behavior for a component out of a given choice, e.g.,        drilldown, set filter, etc.

The extensibility platform herein also uses a Cell-based Architecture.That is, the extensibility platform herein is a cloud-native product,and it scales according to a cell-based architecture. In a cellarchitecture, in particular, the “entire system” (modulo globalelements) is stamped out many times in a given region. A cellarchitecture has the advantages of limiting blast radius (number oftenants per cell affected by a problem), predictable capacity andscalability requirements, and dedicated environments for biggercustomers.

FIG. 14 illustrates an example of a deployment structure of anobservability intelligence platform in accordance with the extensibilityplatform herein, and the associated cell-based architecture. As shown inextensibility platform diagram 1400, an extensibility platform 1410 hascommunity modules 1412 (dashboards, topology), a flexible meta model(FMM) 1414, an OCP 1416, and a UQL 1418. A UI 1420 interfaces with theplatform, as well as an IDP (Identity Provider) 1425. CloudStorage/Compute 1430 has various Applications 1432 (and associated APIs1434). as well as Data Streaming services 1436. A ContainerOrchestration Engine 1440 (e.g., K8s) may have numerous deployed Agents1442. The MELT data is then pushed or pulled into a particular Region1450 and one or more specific Cells 1460. Each cell may contain variousfeatures, such as, for example:

-   -   SecretStore (cloud keys) 1442, Large Scale Data Collection 1444    -   API Gateway 1446    -   Open Telemetry Native Ingest 1448    -   AuthZ (authorization) 1452    -   UQL 1454    -   Unified Query Engine 1456    -   Audit 1458    -   Alerting 1462    -   Health Rules 1464    -   IBL 1468    -   Metering 1472    -   System Event Bus 1474    -   Internal Logs 1476    -   Data Science 1478    -   SQL Query 1480    -   Metrics 1482    -   Events 1484    -   Logs 1486    -   Traces 1488    -   Topology 1490    -   data-as-a-service 1492    -   Kubernetes+ISTIO Service Mesh 1494    -   CNAB (pushbutton install) 1496    -   Data Sync & Migration 1498    -   Etc.

Global control plane 1470 may also contain a number of correspondingcomponents, such as, for example:

-   -   IAM (Identity and Access Management) 1471    -   Feature Flags 1473    -   Authz Policy Templates 1475    -   Federated Internal Log Search 1477    -   Licensing Rules/Metering 1479    -   Monitoring 1481    -   Global event bus 1483    -   GitOps fleet management 1485    -   Environments Repository 1487    -   Etc.        Note that the global control plane 1470 passes Custom        Configurations to sync into the Cell 1460 (data sync &        migration), as shown.

Note that a specific challenge in certain configurations of this modelmay include the balancing of resources between the multiple tenantsusing a cell, and various mechanisms for performing service ratelimiting may be used herein.

Another specific challenge in this model is in regard to disasterrecovery. Again, various mechanisms for disaster recovery may be usedherein, as well.

The techniques described herein, therefore, provide for an extensibilityplatform, and associated technologies. In particular, the techniquesherein provide a better product to customers, where more features areavailable to users, especially as feature development is offloaded froma core team to the community at-large. The extensibility platformprovides a clean development model for first party apps (e.g., EUM,Secure App, etc.) and second party apps (e.g., observability, etc.),enabling faster innovation cycles regardless of complexity, particularlyas there is no entanglement with (or generally waiting for) a core teamand roadmap. The techniques herein also enable a software as a service(SaaS) subscription model for a large array of features.

FIGS. 15A-15D illustrate another example of a system for utilizing anextensibility platform. For clarity purposes, FIGS. 15A-15D eachillustrate a respective quadrant of the entire system. For example, FIG.15A illustrates a first quadrant 1500 a of the system, FIG. 15Billustrates a second quadrant 1500 b of the system, FIG. 15C illustratesa third quadrant 1500 c of the system, and FIG. 15D illustrates a fourthquadrant 1500 d of the system.

The system may receive input from a customer and/or admin 1501 of thesystem. via an admin user interface 1502. The system may include aglobal portion. This global portion may include an audit component. Theaudit component may include an audit query service 1503 that may allowthe querying of an audit log, an audit store 1504 (e.g., dashbase),and/or an audit writer service 1505 that may populate the audit store1504. In addition, the global portion may include Zendesk 1518 oranother component that will support requests, “AppD university” 1519 oranother component that will manage training material and courses,salesforce 1520 or another component that allows management ofprocurement and billing, and/or a tenant management system 1517 formanaging tenant and license lifecycle. An “AppD persona” 1522 mayinteract with salesforce 1520. The global portion may additionallyinclude domain events 1506 for global domain events and identity andaccess management 1507 that facilitates management of users,application, and their access policies and configure federation.

The system may also include external IdP 1512 which may include a SAML,OpenIS or OAuth2.0 compliant identity provider. The system may includeOkta 1511 which may include an identity provider for managed users. Inaddition, the system may interface with OT data source 1529 which mayact as an OT agent/collector or a modern observability agent. In variousembodiments, the system may interface with public cloud provider 1530such as AWS, Azure, GCP, etc. The system may also include BitBucketrepository 1531 to produce configs and/or models as code.

In addition to the global portion, the system may also include a cellportion. The cell portion may include a cloudentity ACP 1508 which mayoperate as an openlD provider, perform application management, and/orperform policy management. Further, the cell portion may includecloudentity microperemeter authorizer 1509 for policy evaluation.Furthermore, the cell may include all services 1510 via envoy proxy.

The cell portion may include a second audit component which may includea second audit query service 1525, a second audit store 1524, and/or asecond audit writer service 1523. The cell portion may also include asecond domain event 1514 for cell domain events. Further, the cellportion may include a tenant provisioning orchestrator 1513, aningestion meter 1516 that meters ingestion usage, and/or a licensing,entitlement, and metering manager 1515 that facilitates queries oflicensing usage, performs entitlement checks, and/or reports on usage.Again, the cell portion may include all stateful services 1528.

The cell portion may include a common ingestion component. The commoningestion component may include data processing pipeline 1533 which mayvalidate and transform data. Data processing pipeline 1533 may alsoenrich entities and MELT based on configurations. The common ingestioncomponent may also include common ingestion service 1532, which mayauthenticate and/or authorize requests, enforces licenses, and/orvalidate a payload.

Moreover, the cell portion may include a common ingestion streamcomponent. The common ingestion stream component may include metrics1547 (e.g., typed entity aware metrics), logs 1548 (e.g., entity awarelogs), events 1549 (e.g., typed entity aware events), topology 1550(e.g., typed entities and associations), and/or traces 1551 (e.g.,entity aware traces). In addition, the cell portion may include a MELTdata stores components that includes metric store 1540 (e.g., druid),log/event store 1541 (e.g., dashbase), topology store 1542 (e.g.,Neo4j), and/or trace store 1543 (e.g., druid).

In various embodiments, the cell portion of the system may include acloudmon component, which may include cloud collectors 1534 that collectdata from public cloud providers 1530. Additionally, the cloudmoncomponent may include connection management 1535, which may facilitatemanagement of external connections and their credentials. In someinstances, the cloudmon component may include a connection store 1536(e.g., postgreSQL).

The cell portion may also include an alerting component. The alertingcomponent may include a health rule processor 1552 for evaluating healthrules and generating entity health events. Further, the alertingcomponent may include a health rule store 1544 (e.g., mongo DB) and/or ahealth rule configuration 1555 that facilitates the management of healthrules. Likewise, the altering component may include an anomaly detectionprocessor 1553 to detect anomalies and/or publish their events, ananomaly detection config store 1545 (e.g., mongoDB), and/or an anomalydetection configuration 1559 that facilitatesenabling/disabling/providing feedback for anomaly detection. Thealerting component may also include a baseline computer 1554 forcomputing baselines for metrics, a baseline config store 1546 (e.g.,mongoDB), and/or a baseline configuration 1560 to facilitateconfiguration of baselines.

The cell portion may include a secret manager service 1537 (e.g.,HashiCorp Vault) exposed to all services 1538 via envoy proxy. The cellportion may include a third domain event 1539 for cell domain events. Inaddition, the cell portion of the system may include a universal queryengine 1556 that may expose a query language for ad-hoc queries. An enduser 1558 may interface with universal query engine 1556 over a productuser interface 1557. In addition, the universal query engine 1556 mayread from schema service 1527. Schema service 1527 may facilitatequerying and management of FMM types. Furthermore, MELT configurationservice 1526 may perform configuration of data processing pipeline 1533.

Other components and interconnections/relationships may be made in anexample extensibility platform herein, and the views and productsillustrated in FIG. 15A-15D are shown herein merely as exampleimplementations that may be used to provide and/or support one or morefeatures of the techniques herein.

——Flexible Meta Model (FMM) for an Extensibility Platform——

The techniques herein extend and/or support the extensibility platformdescribed above by defining a specialized modeling system for MELT dataknown as “Flexible Metadata Modeling” (or Flexible Meta Model) (FMM).The focus here is on the interweaving of multiple models in a matrixform, where one dimension is the different artifact types (MELT,processing pipeline configs, UI configs, etc.) and the other dimensionis the domain (e.g. APM, Kubernetes, End User Monitoring, etc.).Interweaving refers to the fact that at any point in that matrix you canhave safe references to artifacts across both dimensions.

As described in greater detail below, the FMM is based on a system ofentities (‘things’ under observation by the system), with associatedmodel Metrics, Events, Logs and Traces (MELT data). FMM is a system ofdefining Entities, and of defining new types, where each type is one ofthese Kinds: metric; event (includes logs); or trace. FMM also definesrelationships between entities, the entity effectively constituting agraph representing the system under observation.

As also described below, in one embodiment, an implementation of FMMallows the FMM type system to be encapsulated as a system solution,where solutions can create new FMM types, and where solutions canreference and extend the FMM types of the system and other solutions.

To enable FMM, techniques are also presented herein for solutionlifecycles and packaging. That is, a portion of the FMM techniquesherein is based on solutions, along with their packaging, distribution,and dependency model. Solutions have a heavy reliance on the JSON Store,mentioned above, and as described in greater detail below, which makessolution artifacts available to services at runtime.

FIGS. 16A-16B illustrate a diagram representative of a Metrics, Events,Logs and Traces (MELT) data model 1600 herein. (Note that in oneembodiment, the entities in the model 1600 are associated with a single“tenant” in an observability intelligence platform). As shown in themodel 1600 has a data model 1602 and data interrelationship 1604, suchas event types 1606 and events 1616, metric types 1608 and metrics 1618,entity types 1610 and entities 1620, and association types 1612 andassociations 1622. Extension types 1614 are also listed within the datamodel 1602. Various features of the data, including spans 1624 (andlinks 1626 and events 1628), NameValue pairs 1630, 1632, and so on, alsoenhance the data, as detailed therein.

As defined herein, a “source” is what observes a specific data point(e.g., infra-agent, etc.). When multiple sources report data for thesame entity, the techniques herein provide a mechanism to determine thesource of data in order to enable an end user to clearly identify asource of this data, and/or to enable an end user to ‘mute’ a specificsource. Mute, for example, may mean commanding a specific source to stopthe collection (for configurable sources, e.g., observability platformcontrolled agents), or commanding the ingestion pipeline to drop thedata from a specific data source (for non-configurable sources, e.g.,OpenTelemetry agents).

A source should be attached to each reported data point, since it can bedifficult (e.g., impossible) to guarantee that a givenproperty/metric/etc. is always reported by the same source.

In order to identify a source, it is derived in the following order ofprecedence:

-   -   1. From the telemetry.sdk.name attribute in the OpenTelemetry        payload (in order to enable collectors/proxies to propagate the        information about the actual source of the data);    -   2. From the agent type in the application principal's metadata        (agent type is extracted from a specific claim in JSON Web Token        and propagated via the observability-agent-type header);    -   3. Set to sys:unknown.        Once derived, the source is added to each data point propagated        through the platform, and is preserved in each data store. (Note        that source names starting with the “sys:” prefix are reserved        for the platform.) The source values can be further transformed        using source-mapping configs. This allows correction of source        values and mapping to a few standard sources. (Also note that        for the source for derived/generated data (e.g., aggregated        metrics or entities), the source may be set to “sys:derived”.

To define a “kind” herein, each ‘thing’ represented in the ‘data’section in model 1600 of FIGS. 16A-16B is called a kind and has a set ofdefined fields that semantically describes it. Some kinds are typeless,while others must be associated with a type. There are only a few kindsin the platform, and adding a new kind is not a lightweight operation,as it requires adding support for processing and storing that kind inthe data platform. Only the extensibility platform can add new kinds,thus the kinds are considered static.

A “type” is applicable to a single kind and it defines a set ofvalidation constraints for the instances of this type. These validationconstraints are always applied to either specific (extensible) fields ofthese instances, i.e. attributes for entity kind, or to the externaldata that belongs to these instances (e.g., restricting metric typesallowed to be associated with a given entity type). Each type has thefollowing common fields:

-   -   name (uniquely identifies this type; can be used to reference        this type);    -   namespace (a versioned namespace for this type).        A fully qualified type reference is constructed as follows:    -   <namespace>:<name>        If a type is referenced from the same namespace, then        <namespace>: prefix can be omitted.

A “field” is a key/value pair which is specific to a resource that it isassociated with and cannot be modified/extended. Field values can havedifferent types (integer, string, boolean, . . . ) and additionalvalidation rules (e.g., format, pattern, etc.). Field definitions (keysand value types) are always the same across all tenants. Fields aredefined values cannot be modified (since kinds are static). Examples offields follow:

-   -   Entity: id, type, attributes, . . .    -   Association: type, from, to . . .

An “attribute” is a key/value pair which is specific to a resource thatit is associated with. Attributes may be declared in a correspondingtype, which makes them extensible. Attribute values can have differenttypes (integer, string, boolean, . . . ). Attribute values can have highcardinality (potentially a unique value for each instance). Attributedefinitions (keys and value types) declared in a given type are alwaysthe same across all tenants. Attribute values can only be modified bythe source (agent, collector, etc.) that is monitoring that object, orby an extension, they cannot be managed via the UI or APIs. Examples ofattributes:

-   -   Entity of type k8s:pod: name, namespace name, cluster name, . .        .    -   Entity of type apm:service.instance: name, version, . . .

A “tag” is a key/value pair which is not specific to a resource that itis associated with. Tags are not declared, and can only have values oftype string. Tag key is unique in a given resource instance. Same tagscan be associated with multiple resources (e.g., entities or metrics ofdifferent types, etc.). Tags are intended to be used to specifyattributes of resources that are meaningful and relevant to users, butdo not directly imply semantics to the core system. Tags should be usedto organize and to select subsets of resources (and apply access controlrules), not for defining extra information for them. Thus thecardinality of tag values must remain low. Tag keys can differ acrosstenants, or have a different semantic meaning for the same key acrosstenants. Only entities can be tagged directly, other kinds can only betagged based on the related entities via the enrichments. Tags can bemodified by the source that is monitoring that entity, and they can alsobe managed from within the UI/APIs. Examples of tags:

-   -   department:sales    -   environment:production

Table 1 below offers a comparison of each of the terms above:

TABLE 1 Can Can Can Used To Value Delete Add Modify Identify TypesCardinality Predefined? Keys? Keys? Values? Object? Field Any Any Yes,in kind No No Yes Yes (e.g., ID in entity) Attribute String, Any Yes NoYes Yes Yes (e.g., number, (partial), in required Boolean typeattributes in entity) Tag String Low No Yes Yes Yes No

Specifically with regards to kinds, each kind is marked with a lifecyclestatus:

-   -   STABLE—a kind is stable, any new changes will ensure backwards        compatibility;    -   UNSTABLE—a kind is unstable, new changes can break backwards        compatibility, or it can be removed.        If a field is marked UNSTABLE, while the kind is STABLE, that        means that only those fields can break backwards compatibility.

Notably, all timestamps associated with the MELT data must retain theprecision that it was observed at, thus need to be consistently storedin nanoseconds. Since the topology is derived from the MELT data, thetimestamp precision should (e.g., must) remain consistent, and thus alsoshould be stored and queried in nanoseconds.

An entity 1620 (STABLE) represents an observable logical component thatconstitutes the computing environment and/or applications of an platformcustomer. Examples of entities are REST endpoint, Service, Container,Disk, Thread, JVM, Topic, Database, Router, Cache, etc. Some entitiesrepresent a group (aggregation) of a particular type of entities. Forexample, a service is an entity representing a group of serviceinstances. Such entities are derived from other observed entities basedon a configured entity derivation.

An entity 1620 should (e.g., must) always be associated with an entitytype 1610, which defines:

-   -   parentType (optional)        -   a fully qualified type reference to the parent entity type        -   attributes, metric, event and association types are            inherited from the parent type and cannot be overridden    -   attributeDefinitions        -   a definition of attributes that can be used to describe an            entity of this type        -   each attribute has an associated data type        -   at least one attribute must be marked as required        -   required attributes can be used to uniquely identify this            entity    -   metricTypes (optional)        -   a list of metric types that can be associated with this            entity    -   eventTypes (optional)        -   a list of event types that can be associated with this            entity    -   associationTypes (optional)        -   a list of outgoing association types that can be linked from            this entity, with a list of allowed entity types to which            this association can be connected, e.g.,            -   associationTypes:                -   ‘common:consists of’:                -   ‘infra:container’//a k8s pod can only consist of                    containers

An entity 1620 within a given tenant of a given type and the sameidentifying (required) attributes always has the same id. An entityconsists of:

-   -   id        -   a unique identifier of this entity        -   can be used to reference this entity        -   must be globally unique, across all tenants    -   type        -   a fully qualified type reference to the entity type    -   attributes        -   a list of attributes that adhere to the attributeDefinitions            in the entity type        -   if an attribute with a given name is not defined in a type,            it is considered ‘typeless’ and its value will always be a            string        -   each attribute consists of:            -   name (a name of this property)            -   value (a scalar value of this property)            -   source (a source of this property)        -   each attribute is uniquely identified by name and source    -   tags (UNSTABLE) (optional)        -   a list of tags    -   createdAt        -   a timestamp when this entity was created    -   updatedAt        -   a timestamp when this entity was last updated—this is            modified on an update of the entity metadata or any            corresponding MELT data associated with this entity

An entity 1620 may have the following data associated with it via anexternal reference to its unique identifier: metrics (entityId); events(entityId); spans (entityId, derivedEntitylds); associations (from, to).

An extension (UNSTABLE) type 1614 adds attributes or MELT data to one ormore existing entity types with the following rules:

-   -   all extension owned data types (metrics, events) MUST be        declared in the same namespace    -   an extension can extend one or multiple types, it can also        extend all types    -   only one extension in a namespace can amend a given type        -   due to the above, properties and data reported by an            extension can be uniquely identified via that extension's            namespace        -   ext:{extension namespace} is used as a source for all data            produced by this extension    -   attributes and MELT data reported by extensions do not affect        the lifecycle of the entity

The extension type 1614 defines:

-   -   extends (one or more entity types that this extension is        applicable to)        It also defines the same elements as the entity type 1610, but        associated them to all the entity types that it extends, namely:    -   attributeDefinitions    -   metricTypes    -   eventTypes    -   associationTypes        All of the above are optional, but at least one of them must be        present.

An entity 1620 can be connected with another entity via an association1622. Associations (STABLE) are connecting two entities with a directededge, forming a directed connected graph: “topology”. It is possible totraverse this topology by following any of the associations. In atraversal, associations are referenced by their type name.

An association 1622 can represent either static relationships betweenentities, for example:

-   -   consists_of (one to many): a k8s pod consists of multiple        containers    -   relates_to (many to many): an EBS volume can be mounted to        multiple EC2 instances. An EC2 instance can have multiple EBS        volumes        An association 1622 can also represent dynamic relationships,        for example:    -   a service instance interacts with a REST endpoint in another        service instance    -   a service instance updates a record in a database        An association 1622 is generally always derived from MELT data,        either based on a convention, configuration, or via an        extensibility tap. It remains valid until either from or to        entities expire. An association 1622 should (e.g., must) always        be associated with a single association type 1612, which        defines:    -   cardinality        -   cardinality of this association        -   allowed values:            -   ONE_TO_ONE            -   ONE_TO_MANY            -   MANY_TO_ONE            -   MANY_TO_MANY        -   cardinality is enforced at an association type level, which            means that, for example, for an association with a            ONE_TO_MANY cardinality, an entity can have at most one            outgoing association of this type to another entity    -   isHierarchical        -   Whether this association is hierarchical. Can only be true            if cardinality is ONE_TO_MANY or ONE_TO_ONE. A subgraph            formed by hierarchical associations of the same type is            always a directed tree        -   There cannot be more than one incoming association of a            given type with isHierarchical=true for a given entity    -   isContainment        -   Whether this association is a containment between from and            to entities. Can only be true if isHierarchical is true. If            true, the lifecycle of children is tied to the parent        -   There cannot be more than one incoming association,            regardless of type, with isContainment=true for a given            entity

An association 1622 contains:

-   -   type        -   a fully qualified type reference to the association type    -   from        -   a unique identifier of the entity from where this            association is originating        -   this entity must list this association type in its entity or            an extension type    -   to        -   a unique identifier of the entity to which this association            is connected        -   this entity type must be listed in the associationTypes in            the entity or an extension type of the from entity

Not all associations can be uniquely identified, there can be multipleassociations with the same type, from and to fields if the typecardinality is MANY_TO_MANY. For other cardinalities (ONE_TO_ONE,ONE_TO_MANY), an association can be uniquely identified via type, fromand to.

A span 1624 (STABLE) represents an operation within a transaction.Traces are defined implicitly by their spans. In particular, a trace canbe thought of as a directed acyclic graph (DAG) of spans, where theedges between spans are defined as parent/child relationship. Each span1624 encapsulates the following state:

-   -   entityId        -   a unique identifier of the entity that produced this span    -   traceId        -   unique identifier of the trace, used to group all spans for            a specific trace together across all processes    -   spanId        -   unique identifier of this span    -   parentId        -   (optional) unique identifier of the parent span    -   name        -   concisely identifies the work represented by the span, for            example, an RPC method name, a function name, or the name of            a subtask or stage within a larger computation    -   spanKind        -   the type of a span, one of:            -   INTERNAL                -   Indicates that the span represents an internal                    operation within an application, as opposed to an                    operation happening at the boundaries            -   SERVER                -   indicates that the span covers server-side handling                    of an RPC or other remote network request            -   CLIENT                -   Indicates that the span describes a request to some                    remote service            -   PRODUCER                -   Indicates that the span describes a producer sending                    a message to a broker. Unlike CLIENT and SERVER,                    there is often no direct critical path latency                    relationship between producer and consumer spans. A                    PRODUCER span ends when the message was accepted by                    the broker while the logical processing of the                    message might span a much longer time            -   CONSUMER                -   Indicates that the span describes consumer receiving                    a message from a broker. Like the PRODUCER kind,                    there is often no direct critical path latency                    relationship between producer and consumer spans.    -   derivedEntitylds        -   (optional) a set of unique identifiers of the entities that            were derived from this span    -   a startedAt and endedAt timestamp    -   attributes: a list of zero or more key-value pairs    -   a set of zero or more events        -   each event is a tuple (timestamp, name, attributes). The            name must be a string    -   links to zero or more causally-related spans    -   statusCode        -   (optional) one of            -   ok            -   error    -   errorMes sage        -   (optional) a developer-facing human readable error message            tags (UNSTABLE) (optional)        -   a list of tags    -   a source of this span

A metric (STABLE) is a numeric measurement reported for a specificentity. Metrics 1618 can include:

-   -   A numeric status at a moment in time (like CPU % used)    -   Aggregated measurements (like a count of events over a        one-minute time, or a rate of events-per-minute)        A metric 1618 should (e.g., must) always be associated with a        metric type 1608, which defines:    -   category        -   one of: meter_legacy, counter_legacy, rate_legacy,            monotonic_legacy, average, sum, rate, sum_per_instrumented            entity, current_per instrumented_entity, current        -   governs how this metric is consumed by default (how is value            field calculated)    -   contentType        -   one of: sum, distribution, gauge        -   Content type of this metric    -   aggregationTemporality        -   one of “delta”, “unspecified”        -   Aggregation temporality of this metric. For contentType sum            and distribution it will be delta and for gauge it can only            be unspecified    -   isMonotonic        -   Monotonicity property is understood in OpenTelemetry    -   type        -   A primitive type of the metric, allowed values: long, double    -   unit        -   UCUM compliant unit code, i.e. ms, s, min, /s, /min, /h, %    -   ingestGranularities        -   granularities at which this metric can be ingested, in            seconds    -   attributeDefinitions        -   definitions of attributes that can be used to describe a            metric of this type            Each metric 1618 encapsulates the following state:    -   entityId        -   unique identifier of an entity that this metric belongs to    -   type        -   a fully qualified type reference to the metric type    -   source        -   a source of this metric    -   timestamp        -   a timestamp for this metric    -   attributes        -   (optional) a list of key-value pairs            -   must adhere to the attribute definitions in the                corresponding metric type            -   if an attribute with a given name is not defined in a                metric type, it is considered ‘typeless’ and its value                will always be a string    -   tags (UNSTABLE) (optional)        -   a list of tags            Depending on the contentType, a metric 1618 can include one            or more consumptions functions. Also, the same metric type            1608 can be reported on multiple entities and from multiple            sources, and each unique combination of            type+entityId+attributes+source is referred to as a metric            time series.

An event 1616 (UNSTABLE) is a discrete data record with known semanticswhich happened at a moment in time for a specific entity. Events 1616should be used for infrequent things, like a purchase in a vendingmachine, but not for everything that the vending machine does. Forexample, let's say that you want to keep a history of the temperature inthe vending machine. You could store an event 1616 for every minuscule,subdegree shift in temperature, which would quickly fill up even thelargest databases. Or you could instead take a sample of the temperatureat a regular interval. This kind of data is better stored as a metric. Alog record is also an event, however, there is a connotation that asource of a log record is a log. Logs will be distinguished by separateevent types.

An event 1616 may be associated with an event type, which defines:

-   -   attributeDefinitions        -   definitions of the attributes of this event

There is no definition of a uniqueness for an event. Each reported eventis a discrete immutable entry which is stored as-is. It is possible tohave multiple identical events 1616 (same type, timestamp andattributes) stored in the system.

Each event 1616 encapsulates the following state:

-   -   entityId        -   unique identifier of an entity that this event belongs to    -   type        -   (optional) a fully qualified type reference to the event            type    -   timestamp        -   a timestamp when this event has occurred    -   traceId        -   (optional) an identifier of a trace        -   can be set for logs that are part of request processing and            have an assigned trace id    -   spanId        -   (optional) an identifier of a span        -   Can be set for logs that are part of a particular processing            span        -   If spanId is present, traceId should also be also present    -   raw        -   (optional) a raw payload of this event    -   attributes        -   (optional) a list of key-value pairs        -   must adhere to the attribute definitions in the            corresponding event type    -   tags        -   (optional) a list of tags    -   source        -   a source of this event

According to one or more of the embodiments of the techniques herein,the following discussion defines solutions, their lifecycle, theirpackaging, their distribution, and their dependency model. Solutionshave a heavy reliance on the JSON Store (described below) which makessolution artifacts available to services at runtime.

FIG. 17 illustrates an example diagram 1700 of solution packagingaccording to the techniques herein. In the diagram, there is a solutionnamed “XPack” 1710 and a solution named “YPack” 1720 which are shown indetail. We also see two other solutions “QPack” 1730 and “ZPack” 1740which are not shown in detail.

A solution is a grouping of JSON files and folders that is installed orremoved from the platform atomically. As shown below, solutions containsubgroups (folders) of related artifacts:

-   -   FMM models and configs;    -   UI artifacts (visualizations, enrichments); and    -   Platform Connectivity Services (custom REST/GRPC endpoints,        custom cloud collectors).        These artifacts are allowed to reference artifacts in other        solutions packages. The reference mechanism varies based on the        artifact type. For instance, in the FMM, the mechanisms of        extension and Association are used to make references across FMM        namespaces. The diagram 1700 shows links labeled “contains” (a        type of FMM association used in rollups), and “extends” (a        mechanism allowing one solution to add functionality atop        another—this is how the techniques herein create a system        “health” solution that adds health attribution to all system        entities). The FMM has its own type reference system based on        namespaces. A type reference in FMM is encoded as        <namespace>:<type>, therefore Namespace X can extend Y: EntityB        (an entity that lives in another namespace). An FMM namespace is        analogous to a java package, and a type is analogous to a class.        So we see that FMM components can declare a reference to an        artifact defined in another namespace, however, just like in        java, there still needs to be a way to ensure that the necessary        “packages and classes” are present at runtime. In Java there are        systems like Maven that are responsible for identifying and        downloading packages with the required dependencies. The        techniques herein articulate the mechanism in the extensibility        platform by which FMM dependencies are located and installed        into the runtime environment because the FMM itself has no        position on this.

UI artifacts and platform connectivity services also make reference toFMM artifacts. For example, a UI artifact may be powered by a UQL querythat is defined as part of the artifact. The UQL allows the query tospecify a target entity to fetch and the UQL also uses the<namespace>:<type> system of the FMM. However, we could also foreseethat a UI artifact in the XPack solution may want to enrich a UIartifact in the YPack solution. It may not be known what this referencemechanism looks like. A UI artifact is not an FMM model so presumably itcannot be referenced by an FMM reference (<namespace>:<type>). Such areference is shown on the line labeled “enriches” in the diagram 1700.

In order for the system to locate and install all required dependencies(FMM models, UI artifacts, etc.), the package contains a manifest. Themanifest tells the platform what solution dependencies are required.Although there are some approaches to parsing artifacts to determinedependencies, the most straightforward solution is to allow the solutiondeveloper to provide a manifest with a list of dependencies.

A manifest JSON (manifest.json for Xpack Solution) might look like this:

{  ″name″: ″XPack″,  ″version″: ″2.2″,  ″dependencies″: [″YPack″,″Qpack″],  ″description″: ″Provides FMM entities for enhanced MongoDB monitoring″,  ″contact″: ″foo@bar.com″,  ″homepage″:″solutions.appd.com/XPack″,  ″gitRepoUrl″:″https://github.com/XPack/solution″ }

Solution packaging itself is based on a number of defined terms below:

-   -   solution—a collection of files, each file/folder having a        purpose known to the platform.        -   Every solution must have a structure that we can call the            “solution package format or layout”.        -   Solutions can be packaged into a tar-zip (.tgz) file and            hosted in a binary repository such as artifactory        -   A solution .tgz file should be named as            <solution-name>-<major>.<minor>.tgz        -   the solution name should be descriptive; For instance, if a            single FMM model like common:k8s is placed in a solution            with no other artifacts, it probably makes sense to call the            package common-k8s.3.2.tgz    -   solution version—the solution's version is read from        manifest.json        -   FMM namespace is independent of a solution version and has            no impact on solution versioning.    -   Solution Registry—        -   a global registry used to map solutions to tenants (the            mapping constitutes a subscription)        -   the solution registry is integrally linked to the JSON            store. The solution registry's interplay with the JSON store            is shown here in the JSON store docs.        -   this registry can be wrapped in consumer user interfaces and            web pages so that a human “solution owner” can manage the            solution in the registry        -   the solution owner is responsible for updating the solution            if the package changes. The registry keeps (or has a pointer            to) only one version of a solution.        -   The solution registry will record a checksum (and store a            history of version->checksum) over the solution package each            time the solution package changes. If package contents are            changes, this system ensures that the version in            manifest.json must at least be different.        -   The solution registry can perform some basic checks on            artifacts. For example it ensures that for a given FMM            namespace, that there is exactly one solution registered as            the owner of the namespace. This prevents namespace            hijacking in which a 3rd party could alter an FMM namespace            it does not own.        -   All production cells deploy the version of the solution            held/pointed-to by the registry        -   Solution registry allows special test cells to be fooled            into thinking a version of a solution other than current, is            current. This allows test cells to have tenants subscribed            to a version that is not yet promoted to current.    -   FMM namespace        -   An FMM namespace is a collection of all FMM artifact            definitions.        -   An FMM namespace has no required relationship to a solution            name        -   An FMM namespace is deployed to a cell in its entirety, from            a solution version.        -   An FMM namespace's artifacts will never be “cobbled”            together from multiple solution repos.        -   The cell solution syncer (in a cell) tracks a 1:1 mapping            from FMM namespace to repo+sha mapping and will reject any            FMM artifacts that attempt to be deployed “on top of” an            existing FMM namespace. In other words, namespace artifacts            are conveyed as an atomic unit, from a single solution            version, into a cell.        -   An FMM namespace is either present, or absent, in its            entirety on a cell.    -   Subscribing—        -   For a tenant to be subscribed to a solution, the solution            artifacts must exist in the global level of the cell's JSON            store (as mentioned earlier the git repo syncer handles            this; it is part of the JSON store)        -   The first tenant to subscribe to Solution Foo, triggers all            FMM namespaces to sync from Solution Foo and all its            transitive dependencies, into the global level of the cell's            JSON store.        -   The second tenant to subscribe to Solution Foo triggers no            installation actions, since Solution Foo's artifacts are            already installed in the cell's JSON store.        -   Local tenant-to-subscriptions mapping            -   When a solution is “subscribed” by a tenant, it means                that activities described by the solution will be                executed upon that tenant's MELT data. For example, an                FMM namespace may provide a “contains” relationship                performing spatial metric rollup.            -   when MELT data flows through the system is must be                accompanied by sufficient meta data to identify its                tenant            -   conditional execution—execution logic described by FMM                configs for Solution Foo is executed conditionally for                Tenant X based on whether the cell's subscription                registry contains a mapping from Tenant X to Solution                Foo            -   the local subscription registry is implemented as a JSON                document living in the tenant layer of the JSON store                (each tenant has a subscriptions document)    -   Unsubscribing—        -   when a tenant unsubscribes, the mapping of Tenant X to            Solution Foo is removed from the local            tenant-to-subscriptions mapping        -   If no tenant remains subscribed to Solution Foo, Solution            Foo's FMM namespaces can all be atomically removed from the            Json Store's global level (please keep in mind that the            Global level is replicated into each cell, so this amounts            to simply uninstalling all the solution's namespace            artifacts from the local cell)    -   Customizing—        -   FMM configs (not models!) can allow for per-tenant            customization        -   Per tenant customizations are implemented according to the            layering strategy described here.        -   For example            -   a config for trace sampling may include a sampling                frequency. If allowed, each tenant may override the                default.            -   A config may contain default OpenTelemetry metric                attribute names that map a metric to an entity. If                allowed, each tenant may alter the list of attribute                names.        -   Customization implies that configs for actions taken in FMM            pipelines must be dynamically read from the JSON stores            tenant layer where per-tenant changes are applied on top of            the global layer. Pipelines are free to use caching to            optimize, but where allowed in a config, must be prepared            for each tenant to provide a unique value for a given field            in a JSON config.

——Tenant-Specific Solution Subscriptions——

The techniques herein extend and/or support the extensibility platformdescribed above by describing Tenant-Specific Solution Subscriptions(e.g., a JSON Object Store) component of the extensibility platformdescribed herein. In particular, as described below, the JSON storeallows developers to package configs in a prescriptive manner called a“solution”. Solutions are synchronized from a global solution repositoryinto a JSON store of each cell. In the cell, configs are consumed byservices through a uniform JSON store API.

As background, JSON files are needed by many elements of the platform,as well as by end users of the platform. For example, various needs arefor such things as, e.g., backend FMM configs (for example, theattribute to entity mapping config in the common ingest pipeline),dashboard storage, end user preference storage, and so on.

Backend FMM configs are an example of JSON objects that are consumed inmany places by common ingest. The problem is that these configs do nothave a rigorous lifecycle. There is a manual process of “putting configsin place” across various services, which makes it impossible for anyonebut a backend developer to provide configurations. The JSON store solvesthese problems by allowing developers to package configs in aprescriptive manner called a “solution”. Solutions are synchronized froma global solution repository into the JSON store of each cell. In thecell, configs are consumed by services through the uniform JSON storeAPI. FIG. 18 shows a network 1800 of solution developers 1802 (e.g.,1802-1 . . . 1802-N) (including internal system solutions) who are ableto package solution configs 1804. Tenant admins 1806 (e.g., 1806-1 . . .1806-N) are then able to subscribe 1808 (e.g., 1808-1 . . . 1808-N) tosolutions (such as Intersight, a third party app), which results in thesolution configs 1804 being loaded into the JSON store 1810 (e.g.,1810-1 . . . 1810-N) of the cell 1812 (e.g., 1812-1 . . . 1812-N) wherethe tenant resides.

In addition to solution configs 1804, which are accessed by platformservices such as CIS, the JSON store 1810 manages JSON objects such asdashboards that are owned by individual users. This is shown in FIG. 19in the illustration 1900 shows an end-user 1902 interacting with JSONstore 1910, CIS services 1904, and/or service API 1906 within a cell1912. Illustration 1900 shows how the JSON store 1910 manages JSONobjects. These JSON objects are not packaged into solutions, but arecreated directly by the actions of end-users 1902 such as “create newdashboard”. The JSON store 1910 manages the user-to-object mapping,which makes life easier for any platform service that needs per-usercontent. If a service is nothing more than CRUD on a JSON object, thenthe extensibility platform user interface can directly use the JSONstore 1910, without a wrapper service. More complex services will usethe JSON store 1910 both to avoid wheel-reinvention, and to allow theirservice to be configured by solutions. As shown, it may be possible forthe extensibility platform UI web client to directly CRUD a dashboardobject. The illustration 1900 also shows the indirect CRUD model inwhich the “Service X” is standing in front of the JSON store 1910 toprovide complex object validation and other domain logic.

Regarding automatic management of user objects, it is important to notethat the JSON store 1910 transparently manages “ownership” of objects.Whether it is a user's dashboard, or a tenant's pipeline config, theJSON store 1910 automatically recognizes the identity principal of theuser, and uses this information to target the objects owned by thatprincipal. The identity principal can be a tenant itself. This allowsplatform configs to be retrieved by internal services that are using theconfig to provide data processing parameters.

According to the extensibility platform herein and with reference againto the example 1000 of FIG. 10 above, every object in the json store1910 has a type defined by a schema. Solutions can create their owntypes. The JSON store 1910 logically segregates objects of the sametypes into tables. Within a single cell, there can be many JSON stores1910, each operated by a different team. A service mesh istio trafficrule may be used to determine which type-table lives in which store.This is enabled by the JSON store REST API which is structuredas/json/<type>. The <type> in the path is used to route API requests forobjects of different types to the correct underlying JSON store 1910. Inthis manner, if the type is “dashboard”, API requests regardingdashboards are always directed to the JSON store istio virtual serviceoperated by the dashboards team. A given store may hold many differenttype-tables. However, a type-table may live in exactly one JSON store.This design allows teams to operate their own JSON store 1910 withouttaking an operational dependency on other teams.

FIG. 20 illustrates an example architecture diagram 2000 for cell-basedJSON stores. In particular, solutions are collections of JSON content(configs of one form or another). These solutions are managed outside ofthe JSON store, meaning developers 2002 (both internal and external)create these solutions and upload them to a solution registry 2004.However, there is a close tie-in with the JSON store. At runtime,systems that need to consume configs, talk to the JSON store in theircell. This means that solution configs may be required to be synced fromthe solution repository to the local JSON store of a cell (e.g.,synchronization involving solution synchronizer 2020 and/or binaryrepository 2018). A more detailed view of the system shows how asolution is packaged as a binary tgz file, and how the solution registry2004 uses the global cell 2008 domain event bus 2006 to inform cells2010 (2010-1 . . . 2010-N) that a package of solution configs needs tobe redeployed into the JSON store (due to changes in the package). Thedeveloper 2002 may be a third part solution developer who may be free toutilize git but may be required to upload their solution package to thesolution management service 2014 of the system (e.g., via solutionmanagement user interfaces and/or APIs 2016. The diagram 2000 also showsmultiple mongoDB instances 2012 (e.g., 2012-1 . . . 2012-N) that areused as the document stores accessible by JSON service 2021 and/ordomain event bus 2022. As described earlier, document types aresegregated into different physical document stores operatedindependently by domain teams. In order to provide atomic, eventuallyconsistent transactions across stores (which is needed for theinter-object references) the system will provide built in support forsagas. The sagas provides a guarantee of eventual consistency for atomicmulti-store actions such as “insert an entry into Table A, and add areference to from an item in Table B” Where A and B may be in differentdocument stores.

In an example CRUD operation, a user may save changes to a dashboard atweb application 2023. For example, a tenant admin may change a tracesampling frequency.

Due to the distributed nature of the system, there is no way to ensureinstantaneous activation of multiple solution artifacts in multiplestores. Two phase commit (TPC) helps the techniques herein to minimizethe time in which application artifacts are in an inconsistent state(for instance when some but not all the solution artifacts have beenactivated), and also to handle the case when a proposed solutionartifact is in an invalid state (solution developer has made a mistakeor error in their config that cannot be detected before the solution ispublished). In this latter case, there are new or updated solutionartifacts, where one or more of the artifacts is broken, and TPC helpsprevent activation of a broken artifact by progressing in phases:

-   -   1. pre-commit phase—each solution artifact is published on the        bus as part of a “pre-commit” message. The message must have a        TPC id. There will be one message per artifact. And each message        will contain index of the artifact in the solution. When a store        has received all the messages for a solution, it will reply with        ‘pre-commit response’ containing success or failure, depending        on whether all artifacts are valid as per the store, or not.    -   2. commit phase (happy path)—This happens when all the stores        have voted “success”. In response a single commit message is        published on the domain event bus with the TPC id. All stores        must now act on the commit message and commit the updates.    -   3. abort (sad phase, mutually exclusive with commit phase)—in        the pre-commit phase some stores have responded with “failure”.        In response a single “abort” message is published.

In addition, when a proposed solution artifact is in an invalid state,the techniques herein may raise appropriate audit events to alert thesubscriber and possibly the solution developer that a TPC has beenaborted or did not complete due to timeouts of ack's.

As will be appreciated, the JSON stores files, but it is more than justthat. If a file has no default values, or simple defaults that can becaptured in its JSON schema, then the JSON store can store that file assimple JSON “blob”. However, more complex cases for defaults, andoverriding defaults are common. For example, consider the common casewhere each tenant wants to set the default timezone shown in the userinterface for users of that tenant, but each end user can override thetimezone setting. The JSON store is built to accommodate these scenarioswith a concept called “layering”.

FIGS. 21A-21E illustrate an example 2100 of layering within the JSONobject store (with FIGS. 21B-21E illustrated zoomed-in quadrants of FIG.21A). Layering means that the JSON document (e.g., complete document2102 (e.g., 2102-1 . . . 2102-N)) returned to the caller (e.g., user2108 (e.g., 2108-1 . . . 2108-N)) is assembled at read-time by composinga hierarchy of document fragments 2104 (e.g., 2104-N). As illustrated inFIG. 21B, a JSON store may store the object fragments 2104 at differentlevels of ownership. The fragments 2104 by themselves are partialdocuments. The JSON store may lazily assemble JSON documents bycombining one fragment 2104 from each level to create a completedocument 2102 which conforms to a descried JSON schema. That is, whenthe fragments 2104 are assembled in layers, a complete document 2102results. Layering allows for mutable defaults. With this model, asolution can provide default UI settings at the Global level 2110, andagain at the account level 2120. At the tenant level 2130, each tenantcan save a fragment that overrides a field, for example the timezonesetting. And at the user level 2140, end-users can adjust their timezoneor any other user-mutable settings. When a user queries the JSON storefor their UI settings, they receive a settings object that complies withthe json schema provided by the solution for user settings.

FIG. 21C illustrates a specific example of how each UI config object2112 (e.g., 2112-1 . . . 2112-N) is stored in terms of a time zoneand/or theme configuration. A global default (e.g., saved as UI configobject 2112-1) may be stored at the global level 2110. At the tenantlevel 2130, different tenants (e.g., tenant A and tenant B) have eachmade their own customizations to the defaults (e.g., saved as UI configobject 2112-3 and UI config object 2112-4). At the user level 2140, manydifferent users may have saved their own values for the time zone and/ortheme configurations (e.g., saved as UI config object 2112-5, UI configobject 2112-6, UI config object 2112-7, and UI config object 2112-4).

FIG. 21E then shows how the UI config objects 2112 of User X (e.g., user2108-2) and User Z (e.g., user 2108-N) are assembled, respectively. Eachfinal document 2102 may be built from the fragments 2104 that trace apath from a global object (e.g., UI config object 2112-1), through thetenant object (e.g., UI config object 2112-3 and UI config object2112-4) where the corresponding user 2108 resides, and down to a leafuser object (e.g., UI config object 2112-5 and UI config object 2112-N)of the corresponding user 2108.

The fragment model exists to support object models that requiredefaults, but it does not force that model onto consumers. In otherwords, if a service needs to store an object it is free to store acomplete object and not to use layering.

In addition to the user-local layer, as shown in the alternative example2200 of FIG. 22 , fragments 2202 (e.g., 2202-1 . . . 2202-n) can belayered among a user-global layer 2210 used for user objects like UIpreferences that must follow the user to any tenant the user logs into.Account layer 2220, and user-global layer 2210 are replicated globallyon the global event bus (global level 2230).

As defined herein, therefore, the following layers/levels can be usedwith the layering model herein:

-   -   Global Level—a fragment (e.g., fragment 2202-1) in the global        level can be either:        -   a true singleton object (some kind of global setting. For            instance the Disaster Recovery service could use a global            object to store backup snapshot frequency. It is literally a            single value that is needed by every cell.)        -   a “default” for tenant scope objects like            tenant-customizable ingestion configs.    -   Account Level—account level fragments (e.g., fragment 2202-2)        allow each account to have independent settings.    -   Tenant Level—tenant level fragments allow each tenant to have        independent settings.        -   The EUM application will allow tenants to save a custom            trace sampling frequency. Any settings that affect the            ingest pipeline are likely to be either global, or per            tenant. (It is unlikely that individual users will have            customized ingest pipelines.)        -   A particular tenant admin may decide that she wishes to            change the default of “enable_help_popups”. This change will            “shadow/override” the global value, for her tenant, thus            customizing the application for her tenant.    -   User-global Level—user-global level fragments (e.g., fragment        2202-3 and fragment 2202-N) are likely to be used for end-user        specific settings such as personalized true/false for        ‘enable_help_popups’ and many other user UI preferences that        should be available in any cell the user can log into.    -   User-local level—user-local level fragments are used to save        objects that are specific to a cell, such as a tenant-specific        dashboard.

Note that one of the key reasons for layering is to support global-wideand tenant-wide changes easily. Layering is a form of normalization ofdata. Layering allows a solution to make a change in a single place, andhave that change affect all tenants and users, if desired. This isoptimal because it keeps solution management simple. The solution ownerdoes not need to adjust defaults in every tenant. They use the globallayer to make a single adjustment, and rely on the JSON store to ensurethat every tenant receives the new value.

Further according to the techniques herein, FIG. 23 illustrates anexample of a logical model 2300 that defines the relation betweenvarious pieces of a JSON store. Here it can be seen that Solution 2310defines zero or more Type 2320, e.g., a dashboard solution could definea dashboard type. There could be zero or more instances of dashboardwhich is represented as an Object. Objects 2330 could be instantiated bySolution 2310, Account Admin 2340, Tenant Admin 2350, or Users 2360directly. For example if a solution would like to create built-indashboards, which will be available to all tenants subscribing to thatsolution, those dashboards will be instantiated by Solution. SimilarlyUsers can instantiate more dashboards. Having said that, there could bea few types which can't be instantiated by Account Admin, Tenant Adminor users. The Type definition of those types will annotate the typeaccordingly. Also there could be situations where the single object fora type can exist for a Solution, Account Admin, Tenant Admin or Users.That is defined as a Singleton. (Singleton vs Collection is describedbelow). Type definition of such types will also annotate the typementioning the singleton behavior.

The JSON store 2370 can be thought of as several logically separate datastores; one store for each “type” of object stored in the JSON store.Each type must have a JSON schema, and each type appears in the REST APIfor the JSON store. For instance a system solution called“visualization” contains a type definition called “dashboard”. Thereforall dashboards objects are rooted at the RESTpath/json/visualization/dashboard and must adhere to the schema.However, JSON schema may be insufficient to carry all of the informationneeded to define a type. One place where additional needed metadata notconveyed in a JSON schema has to do with object identities, andreferences.

To access an object in the JSON store via its REST path the object idmust be included. In general, /json/<solution>/<type>/<id>. Consider anREST path:

-   -   /json/shopzilla/shoppingcart/1214ca56-83b9-416f-8536-fa953308429b.        Shown below is what the JSON store would return (for a shopping        cart that only includes one product) for an object reference:

{    lastUpdated:″04:19:21:13 06/12/2021″  product:″fdd3bed-126b-4ee9-a513-ae5f856946fe″,  total: 15.32 }

Looking at the json shopping cart object above, we can guess from thefield named ‘product’ that “fdd3bed-126b-4ee9-a513-ae5f856946fe” is aunique identifier of a product. But the system doesn't (and cannot) relyon any guessing or heuristics. Instead, the solution packaging systemincludes type metadata that tells us very specifically which fields ofthe JSON document are allowed to contain id's (“references”) to otherobjects, and what type of objects the references refer to. The typedefinition of the shopping cart type would have metadata shown below todefine the reference to the object of type product. The“referenceQualifiers” section of a type definition tells the JSON storewhich fields of an object are references that must be “qualified”.Qualifying means that the information shown in the JSON document is anid (aka a ‘reference’ to an object of another type) and must be combined(qualified) with the reference prefix telling the solution and type.

  ″referenceQualifiers″ : {  ″$.product″ : [   ″/json/shopzilla/product″ ] }

In this way, the JSON store knows that the field product is a reference(an id) equal to fdd3bed-126b-4ee9-a513-ae5f856946fe and that this idmust be qualified (combined) with/json/shopzilla/product/to form thefully qualified reference:

-   -   /json/shopzilla/product/dd3bed-126b-4ee9-a513-ae5f856946fe

Additionally herein, references may also point to arrays. Using theunderstanding of references from above, the definition of a shoppingcart can be expanded to include an array of products in the cart: Thisis illustrated in the below array reference:

  {  lastUpdated:″04:19:21:13 06/12/2021″  products: [  ″fdd3bed-126b-4ee9-a513-ae5f856946fe″,  ″0df36d2-4164-4f66-88a3-efd587a306ce″  ]  total: 25.64 }

Regarding dereferenced fetching, the API herein allows for instructingthe server to prefetch references. For instance, imagine the shoppingcart object has a reference array called ‘products’. As such, thetechniques herein can perform an operation toGET/json/shoppingcart/1214ca56-83b9-416f-8536-fa953308429b?prefetch=*.When this is performed, all the object references are prefetched. Notethat instead of the products field having products:“fdd3bed-126b-4ee9-a513-ae5f856946fe”, the content of the products arrayhas been transitively prefetched, as shown in the following prefetchedreference:

 {   lastUpdated:″04:19:21:13 06/12/2021″   products: [{name:foo,cost:0.99, status: ″available″, sellers: [{name: Seller1}, {name:Seller2}]}, {name: bar, cost:14.99, status: ″outOfStock″, sellers:[{name: Seller2}, {name: Seller3}]}]   total: 25.64  }

The techniques herein also allow for being selective to specify exactlywhich fields to dereference:/json/shoppingcart/1214ca56-83b9-416f-8536-fa953308429b?prefetch=products. As with all URL query parameters, the parameter canbe repeated to specify a list of fields to prefetch.

Regarding object spreading herein, consider an Employee objectcontaining a field named ‘team’. The following query, which does not usespreading, shows the dereferenced fetch of the ‘team’ field:/json/Employee/1214ca56-83b9-416f-8536-fa953308429b?prefetch=*. As canbe seen, the ‘team’ object has been prefetched, as in the prior examplesof prefetch.

  {  firstName: ′john′,  lastName: ′doe′  team: {    teamName:″municipal waste cleanup″,    code: ″SFO″   } }

Using the ‘spread’ operator, the team object can be spread into theparent:/json/Person/1214ca56-83b9-416f-8536-fa953308429b?prefetch=*&spread=*.As can be seen below, teamName and region are now spread into theenclosing object.

{    firstName: ′john′,  lastName: ′doe′  teamName: ″municipal wastecleanup″,  code: ″SFO″ }

Spread can also be used to finely control which prefetched fields arespread. For instance, if the Employee had two reference fields, ‘team’and ‘location’, the techniques herein could allow specifying that onlyteam is spread:/json/Person/1214ca56-83b9-416f-8536-fa953308429b?prefetch=*&spread=team.As you can see below, location has been prefetched but not spread.

{    firstName: ′john′,  lastName: ′doe′  teamName: ″municipal wastecleanup″,  code: ″SFO″  location: {    city: ″san francisco″,   state:″ca″  } }

Regarding breadth and depth herein, reference prefetching allows forspecifying the depth of prefetching. That is, one can specify a prefetchdepth (how many object references are pursued) as well as a breadth (howmany array items are prefetched in the case of array references). Forinstance, suppose that a product type exists, and that the product typecontains an array of sellers, and a status (as shown above). However,assume that seller is a type, that is itself referenced from a product.One can then make this API call toGET/json/shopzilla/shoppingcart/1214ca56-83b9-416f-8536-fa953308429b?prefetch=true?breadth=1&depth=2.This will result in one array product being prefetched (breadth=1) intoany array, such as in the breadth and depth limiting example below.

{  lastUpdated:″04:19:21:13 06/12/2021″  products: [{name:foo,cost:0.99, status: ″available″, sellers: [{ }]}]  total: 15.32 }

Furthermore, it can be seen that the content of the seller array has notbeen filled, because the depth=2 parameter prevents the prefetching ofseller content (the products object, and the seller array have alreadybeen prefetched). Also note that the seller array contains only oneobject (breadth=1).

To update simple fields and references, objects may be updated usingJSON patching. For example, this Merge Patch tells the server to changethe total field to 20 (updating a single field):

{    total: 20.00 }

The platform herein can also update reference fields. For instance, thisis a Merge Patch that can be PATCHedto/json/shopzilla/shoppingcart/1214ca56-83b9-416f-8536-fa953308429b,which will update the total, and point the product to a differentproduct object:

  {  product: ″fdd3bed-126b-4ee9-a513-ae5f856946fe″  total: 15.32 }

Note that Patches cannot be applied to fields that werereference-prefetched such as products, unless the update is a validreference (as in the first example above). For instance, the followingJSON Merge Patch will fail because products is a reference field (i.e.,prefetched references cannot be updated):

{  lastUpdated:″SOME NEW DATE″  products: [{name:foo, cost:0.99, status:″available″, sellers: [{name: Seller1}, {name: Seller2}]}]  total: ″SOMENEW PRICE″ }

If one wishes to update the products array, they must keep in mind thatproducts is an array of product objects locatedat/json/shopzilla/product. To add an item to the array, we can use anRFC 6902 JSON Patch which must be PATCHed to the

-   -   “fdd3bed-126b-4ee9-a513-ae5f856946fe”        That is, to add an element to an array:

 [   { ″op″: ″add″, ″path″: ″/products/-″, ″value″: ″fdd3bed-126b-4ee9-a513-ae5f856946fe″ }  ]On the other hand, to empty products arrayin/json/obj/shoppingcart/1214ca56-83b9-416f-8536-fa953308429b, thefollowing patch can be used (this patch will fail):

[    { ″op″: ″replace″, ″path″: ″/products″, ″value″: [ ]} ]

According to the techniques herein, the JSON store can act as a facadefor values that are stored in other systems. For example, anillustrative system herein may support marking certain fields as“secrets”. A secret field is stored in vault and is never actuallyplaced into a solution or JSON Store. The JSON store will just store anID of the secret stored in the vault. However, clients with sufficientprivileges can read fields from the json store that are secret. Based onthe RBAC, the JSON Store will pull a secret in the real-time from thevault and return in data.

For security reasons, a solution developer should not be able to store asecret in a solution. This is because a solution is a file containingtype and code artifacts, and will be potentially stored in not-so-securestorage engines or any other Solution Registry. With this in mind,solution developers are expected to create/update the secrets out ofband.

On the other hand, tenants may always call the JSON store API to managesecrets. In addition to marking certain fields as secret, there is asystem solution in an illustrative embodiment which provides a “Secret”Object Type which has fields like—name of secret, value of secret,expiry datetime, etc. Only value is marked as a secure field, and willbe stored in vault, while maintaining its reference in a Secret object.Solution developers will be allowed to ask for reference to a secretobject as part of subscription requirements.

Notably, in certain embodiments, the JSON Store provides a data modelfor RBAC via a system solution. This data model makes no prescription asto how it is used to enforce RBAC. It simply records facts about who isallowed what. The permission model is designed so that permissions canbe assigned to any kind of resource (be it a JSON store object or arest/http endpoint). For example the information could be used by a RESTproxy to enforce the http permission kind.

The illustrative RBAC package provides three core types: permissions,roles, and roleAssignments.

A “permission” is a combination of particular kind, and access actionsalong with its context. An example permission on a type of object in thejson store may be seen below:

{   name: can_edit_dashboard,   displayName: ″Can Edit Dashboard″,  kind: jsonStore,  actions: [{    verbs: [create, update],    context:{       solution: visualization,     type: dashboard    }   }] }As an example permission on an http path (a REST resource):

{   name: can_launch_investigation,   displayName: ″Can LaunchInvestigation″,   kind: http,  actions: [{    verbs: [PUT, POST],   context: {      path: /investigation,     endpoint:<customer>.observe.appdynamics.com/ext/securityboss    }   }] }As an example permission on a set of data, identified by tags:

  {   name: qa_observerer,   displayName: ″Can see read data from QAcluster″,   kind: tags,  actions: [{    verbs: [read],    context: {    tags: [qa_logs, qa_cluster4_logs]    }   }] }A “role” is a name for a set of permissions. This could be defined atsolution, account, or tenant layer. For example:

  {  name: security_investigator,  displayName: ″Security Investigator″, permissions: [   investigation:can_launch_investigation,  investigation:can_close_investigation,  investigation:can_assign_investigators,  investigation:can_add_asset_toInvestigation  ] }

Regarding “roleAssignments”, an assignment is simply a link between auser (or group), and a role. Assignments happen at the tenant level. Butit can also be defined at the account level. Role assignments can beused in a solution package to assign a role (defined in the solution) toan existing group.

Note that solution packages are not allowed to include role assignmentsthat are automatically invoked. There must be an intermediation processinvoked when a solution is installed in the cell that prompts theinstaller to accept or reject any role assignments proposed in thesolution. The person subscribing to the solution can be asked to “acceptall” or cherry pick the proposed assignments. Essentially the solutionhas recommendations, and the subscriber is popped to a screen thatallows them to easily accept or modify the proposal.

As an example:

 { assignedTo: { type:user, identity:fred@coke.com},assignedRoles:[investigation:security_investigator, visualization:dashboard_admin]}  { assignedTo: { type:group,identity:″appdynamics.defaultgroups.  poweruser″}, assignedRoles:[investigation:security_investigator, visualization: dashboard_admin]}

When a user is trying to create an object of a type ‘type_X’, the belowset of actions will need to be done to evaluate whether the user isallowed to act on it:

-   -   1. Find the groups which the user is part of (e.g., extracting        this information from a JWT or synced in JSON Store from IDP).    -   2. Find the roles assigned to the user and the assigned groups.    -   3. Get permissions with ‘resources’ containing ‘type_X’ and        ‘verbs’ containing ‘create’.    -   4. Filter the roles found in step 2 which has permissions found        in step 3.    -   5. If there is even a single role, object creation on ‘type_X’        will be allowed.

Notably, in addition to these three core types, the RBAC solution hereinmay manage two more types:

-   -   user—This type will include the emailId of the logged in user,        so that admin can refer this user in the role assignment or        group assignment.    -   group—This type will try to group one or more users/groups into        a collection, so that the collection could be collectively        assigned a role in the roleAssignments table.

Fine-grained RBAC may be available herein through object ownership. Thatis, fine-grained RBAC requires that the provenance of every object isknown. To that end, every object in the JSON store may be created withan internal field called ‘_owner’ that records the identity of thecreator of the document. The presence of this data in every recordallows the JSON store to know what objects belong to whom:

  _owner:{  id: janedoe@coke.com,  type: tenantUser }However, what might not be self-evident is that there are cases when theowner isn't an end-user. These correspond to the solution and tenantlayers in the JSON store. In cases of objects included in a solution theowner may look like this:

  _owner:  id: developer1@appdynamics.com,  type: developer }Also, in cases where the object was created by a tenant service account,it may look like this:

  _owner:{  id: <tenant_id>,  type: tenantService }Apart for storing owner info, each object may store level information,which would define what is the level of object in the level hierarchy.One such example of the level of a solution level object is as below(which would help in allowing co-developers in same developer account toedit solution level data—similarly for the tenant level objects):

  _level: {  layer: ′solution′,  id: ′visualization′ }

Lastly, for object sharing, the_owner field and_level field issufficient to allow the json store to handle the common use case ofshowing a user her own content.

However, in the cases where the user wants to share content with anotheruser or group, the techniques herein may also include within each objecta_share field:

 _share:[   { assignedTo: { type:user, identity:fred@coke.com},assignedRoles:[investigation:security_investigator,visualization:dashboard_ admin]}   { assignedTo: { type:group,identity:″appdynamics.rbac.poweruser″}, assignedRoles: [investigation:security_investigator, visualization: dashboard_admin]}  ]

FIG. 24 illustrates an example simplified procedure for implementing anextensibility platform in accordance with one or more embodimentsdescribed herein. For example, a non-generic, specifically configureddevice (e.g., device 200) may perform procedure 2400 by executing storedinstructions (e.g., extensibility platform process 248). The procedure2400 may start at step 2405, and continues to step 2410, where, asdescribed in greater detail above, a process may include providing acore technology stack for an extensibility platform. The extensibilityplatform may include a multi-celled architecture and the process mayfurther include synchronizing a local copy of the one or more globallyshared core solution packages across each globally distributed cell ofthe multi-celled architecture; and maintaining, within each globallydistributed cell of the multi-celled architecture, one or more specifictenant-based solution packages based on one or more specific tenantscorresponding to that globally distributed cell.

At step 2415, as detailed above, the process may include managing aplurality of solution packages within the extensibility platform thatare separated from the core technology stack, each of the plurality ofsolution packages defining a data model, access to that data model, anddependencies for that data model, wherein the plurality of solutionpackages have one or more globally shared core solution packages.

As noted above, at step 2420 the process may include operating,according to one or more tenant-based solution packages within theplurality of solution packages within the extensibility platform, theone or more tenant-based solution packages defining correspondingtenant-specified models and configurations for soft-coded customizedextension points for the extensibility platform. The one or moretenant-based solution packages may include one or more container images.The process may, in various embodiments, include receiving configurationof tenant-specific iconography as part of the one or more tenant-basedsolution packages; and displaying the tenant-specific iconography withina tenant-specific interface according to the one or more tenant-basedsolution packages. The soft-coded customized extension points may beselected from a group consisting of: a model-based extension pointdefining one or more of entity types, association types, and metrictypes; a pre-ingestion-based extension point defining one or more ofcollection configuration, agent configuration, and pre-ingestiontransformations; a processing-based extension point defining one or moreof mapping rules and processing rules; and a consumption-based extensionpoint defining one or more of interface configuration, reportconfiguration, and webhook configuration.

Further to the detailed disclosure, the process may include managingmulti-tenancy of an observability data ingestion pipeline of theextensibility platform according to the plurality of solution packagesincluding the soft-coded customized extension points for theextensibility platform. In various embodiments, managing themulti-tenancy of the observability data ingestion pipeline may includeobtaining observability data according to large-scale data collection;mapping the observability data to a plurality of entities according tomodels as defined within the plurality of solution packages, and/orprocessing the observability data for each tenant of the extensibilityplatform according to the core technology stack, the one or moreglobally shared core solution packages, and the one or more tenant-basedsolution packages including their soft-coded customized extension pointsfor that tenant. The observability data ingestion pipeline may be basedon metrics, entries, logs, and trace.

The process may further include determining an object type of particulartraffic within the observability data ingestion pipeline and/or routingthe particular traffic within the extensibility platform to a specificobject store based on the object type of the particular traffic. Theprocess may also include providing an object layering system based on aglobal layer of settings and fields and global layer of applications andsolution constructs, an account layer of multi-cell tenants, a tenantlayer, and/or a user layer. The object layering system may providehierarchical modification control between layers.

Additionally, the process may include providing subscription-basedaccess for tenants to subscribe to registered third-party solutionpackages of the extensibility platform. The process may include groupingentities for composite observability data processing. Further, theprocess may include processing queries into observability data and/orentities based on the observability data ingestion pipeline. The processmay also include providing processed assessments of a status of entitiesbased on the observability data ingestion pipeline. In variousembodiments, the process may include processing custom logic provided asa container image to expose a service interface for execution within acontrolled sandbox of the extensibility platform. Further, the processmay include, evolving the core technology stack for the extensibilityplatform without affecting the plurality of solution packages within theextensibility platform.

Furthermore, the process may include, updating one or more of theplurality of solution packages within the extensibility platform withoutaffecting the core technology stack for the extensibility platform. Invarious embodiments, the process may include receiving updates for oneor more of the plurality of solution packages from any of a first-partydeveloper, a second-party developer, or a third-party developer.

The simplified procedure 2400 may then end in step 2430, notably withthe ability to continue managing solution packages. operatingtenant-based solution packages, and/or managing multi-tenancy ofobservability ingestion additionally accounting for any updates. Othersteps may also be included generally within procedure 2400.

It should be noted that while certain steps within procedure 2400 may beoptional as described above, the steps shown in FIG. 24 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, introduce mechanismsimplementing an extensibility platform. The extensibility platform mayprovide a solution packaging system that allows for data-typedependencies. As such, the described techniques provide a mechanism thatfacilitates coordination of data handling when adapting or extendingsolutions operating across distributed systems. In particular, thetechniques may accommodate a centralized development process utilizingthe data-type dependencies allowing modules to have dependencies liketraditional code/packaging systems while simultaneously allowing themodels to define their data model, access to that data model, packagingof objects conforming to other data solution models, etc.

FIG. 25 illustrates an example simplified procedure for utilizing FMMfor an extensibility platform in accordance with one or more embodimentsdescribed herein. For example, a non-generic, specifically configureddevice (e.g., device 200,) may perform procedure 2500 by executingstored instructions (e.g., extensibility platform process 248). Theprocedure 2500 may start at step 2505, and continues to step 2510,where, as described in greater detail above, a process may includeproviding an extensibility platform for observing a plurality ofentities to produce associated observability data, the extensibilityplatform configured in part by one or more tenant-specific solutionpackages. The observability data may comprise metrics, events, spans,and directional associations. In various embodiments, the observabilitydata may comprise metrics, events, logs, and traces.

At step 2515, as detailed above, the process may include installing aplurality of flexible meta models within the extensibility platform,wherein the plurality of flexible meta models define the plurality ofentities, a globally unique identifier of each of the plurality ofentities, a type of each of the plurality of entities, relationshipsbetween the plurality of entities, kinds of observability data, anddependencies among the plurality of flexible meta models. In variousembodiments, the relationships between the plurality of entities maycomprise how one entity is hierarchically related to another entity. Inaddition, the relationships between the plurality of entities maycomprise how one entity interacts with another entity.

Entity types may further comprise one or more of: a parent type, ametric type, an event type, or an association type. The type of each ofthe plurality of entities may provide validation constraints to beapplied to instances of that type of entity to ensure attributes of anentity adhere to its entity kind, and to restrict metric types that areallowed to be associated with that type of entity. A particular entitymay be defined as an aggregation of a particular plurality of entitiesof a particular type. The plurality of entities may be selected from agroup consisting of a service, a service instance, a businesstransaction, a host, a representational state transfer endpoint, acontainer, a disk, a thread, a java virtual machine, a topic, adatabase, a router, and a cache.

Kinds of observability data may be defined as one of either a metric, anevent, or a trace. In some examples, kinds of observability may bestatically set within the extensibility platform. The plurality offlexible meta models may be defined within the one or moretenant-specific solution packages. Each flexible meta model of theplurality of flexible meta models may correspond to a particular tenantin a multi-tenant architecture for the extensibility platform.

As noted above, at step 2520 the process may include processing theobservability data obtained within the extensibility platform based onthe one or more tenant-specific solution packages and the plurality offlexible meta models, wherein the observability data is associated tothe plurality of entities based on external references to correspondingglobally unique identifiers of the plurality of entities, and whereinthe observability data is sourced by a plurality of sources configuredto populate, based on a corresponding observed entity, one or moreattribute fields and one or more tenant-specified tag fields accordingto that corresponding observed entity.

The process may also include installing user interface artifacts withinthe extensibility platform. Further, the process may include locatingthe plurality of flexible meta models according to a manifest within oneor more of the one or more tenant-specific solution packages.Furthermore, the process may include processing, from a particularflexible meta model, an extension that either adds one or moreattributes or specific observability data to an existing entity type.

In some embodiments, the process may include determining a particularsource of particular observability data. Determining the particularsource may be based on one of a telemetry software developer kit namewithin a payload or an agent type within metadata.

The simplified procedure 2500 may then end in step 2525, notably withthe ability to continue updating tenant-specific solution packageconfigurations and/or FMMs and processing the observability dataobtained based thereupon. Other steps may also be included generallywithin procedure 2500.

It should be noted that while certain steps within procedure 2500 may beoptional as described above, the steps shown in FIG. 25 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, introduce mechanisms forutilizing FMM for an extensibility platform. In particular, thetechniques herein are directed toward a specialized modeling system forMetrics, Events, Logs, and Traces (MELT) data known as “FlexibleMetadata Modeling” FMM. These techniques facilitate the interweaving ofmultiple models in a matrix form, where one dimension is the differentartifact types (MELT, processing pipeline configs, user interfaceconfigurations, etc.) and the other dimension is the domain (e.g.observability intelligence platforms, container orchestration engines,end user monitoring, etc.). Interweaving refers to the fact that at anypoint in that matrix there can be safe references to artifacts acrossboth dimensions. The extensibility platform may provide a solutionpackaging system that allows for data-type dependencies. As such, thedescribed techniques provide a mechanism that facilitates coordinationof data handling when adapting or extending solutions operating acrossdistributed systems. In particular, the techniques may accommodate acentralized development process utilizing the data-type dependenciesallowing modules to have dependencies like traditional code/packagingsystems while simultaneously allowing the models to define their datamodel, access to that data model, packaging of objects conforming toother data solution models, etc.

In closing, FIG. 26 illustrates an example simplified procedure forutilizing tenant-specific solution subscriptions for an extensibilityplatform, in accordance with one or more embodiments described herein.For example, a non-generic, specifically configured device (e.g., device200) may perform procedure 2600 by executing stored instructions (e.g.,extensibility platform process 248). The procedure 2600 may start atstep 2605, and continues to step 2610, where, as described in greaterdetail above, a process may include providing access to a plurality ofsolution packages in a global repository associated with anextensibility platform, wherein the extensibility platform is amulti-celled architecture, the plurality of solution packages havingspecific configurations for execution of the extensibility platform. Theplurality of solution packages may be established into the globalrepository by one or more of first-party developers, second-partydevelopers, or third-party developers.

The specific configurations may comprise a dashboard. The dashboard maycomprise a user-specified dashboard. In various embodiments, one or moreof the specific configurations may comprise tenant-specificconfigurations. In addition, one or more of the specific configurationsmay define flexible meta models. In some instances, one or more of thespecific configurations may define a data ingestion pipeline for theextensibility platform. The global repository may comprise a JavaScriptObject Notification store.

At step 2615, as detailed above, the process may include determining oneor more tenants of a particular cell of the multi-celled architecture.The particular cell may comprise a plurality of data stores, and whereinsynchronizing the one or more particular solution packages of theplurality of solution packages from the global repository to theparticular cell is based on any of the plurality of data storesrequiring the one or more particular solution packages.

In various embodiments, determining one or more tenants may be based onrecognition of an identity principal of a given user of the particularcell. The one or more tenants may comprise one of either a user or anorganization.

As noted above, at step 2620 the process may include synchronizing oneor more particular solution packages of the plurality of solutionpackages from the global repository to the particular cell based on oneor more tenants of the particular cell and subscriptions of the one ormore tenants to the one or more particular solution packages. The one ormore particular solution packages may define type metadata that specifytenant-specific fields and associated processing of the tenant-specificfields.

The process may include storing a plurality of model layers consistingof hierarchically mutable settings; and generating a layered model as aparticular solution package of the plurality of solution packages basedon aggregating tiered fragments of the plurality of model layersaccording to the one or more tenants of the particular cell. Theplurality of model layers may comprise a global layer, an account layer,a tenant layer, and a user layer.

In addition, the process may include preventing activation of invalidartifacts within the plurality of solution packages based on a two-phasecommit process. Access may be provided via a repository applicationprogramming interface. The process may include performing role-basedaccess control against objects within the specific configurations. Insome examples, the process may include storing secrets on the globalrepository that remain unshared across tenants and are separate from theplurality of solution packages.

The simplified procedure 2600 may then end in step 2625, notably withthe ability to continue synchronizing particular solution packages fromthe global repository to the particular cell based on updates to tenantsof the particular cell and/or updates to the subscriptions of the one ormore tenants to the one or more particular solution packages. Othersteps may also be included generally within procedure 2600.

It should be noted that while certain steps within procedure 2600 may beoptional as described above, the steps shown in FIG. 26 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein. Moreover, while procedures 2400, 2500,and/or 2600 are described separately, certain steps from each proceduremay be incorporated into each other procedure, and the procedures arenot meant to be mutually exclusive.

The techniques described herein, therefore, introduce mechanismstenant-specific solution subscriptions for an extensibility platform aredescribed herein. In particular, the techniques herein are directedtoward a JavaScript Object Notation (JSON) store that allows developersto package configs in a prescriptive manner called a “solution”.Solutions are synchronized from a global solution repository into theJSON store of each cell. In the cell, configs are consumed by servicesthrough the uniform JSON store application programming interface (API).The described techniques provide a mechanism that facilitatescoordination of data handling when adapting or extending solutionsoperating across distributed systems. In particular, the techniques mayaccommodate a centralized development process utilizing the data-typedependencies allowing modules to have dependencies like traditionalcode/packaging systems while simultaneously allowing the models todefine their data model, access to that data model, packaging of objectsconforming to other data solution models, etc.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative extensibility platform process 248, which may includecomputer executable instructions executed by the processor 220 toperform functions relating to the techniques described herein, e.g., inconjunction with corresponding processes of other devices in thecomputer network as described herein (e.g., on network agents,controllers, computing devices, servers, etc.). In addition, thecomponents herein may be implemented on a singular device or in adistributed manner, in which case the combination of executing devicescan be viewed as their own singular “device” for purposes of executingthe extensibility platform process 248.

According to the embodiments herein, an illustrative method herein maycomprise: providing, by a process, an extensibility platform forobserving a plurality of entities to produce associated observabilitydata, the extensibility platform configured in part by one or moretenant-specific solution packages; installing, by the process, aplurality of flexible meta models within the extensibility platform,wherein the plurality of flexible meta models define the plurality ofentities, a globally unique identifier of each of the plurality ofentities, a type of each of the plurality of entities, relationshipsbetween the plurality of entities, kinds of observability data, anddependencies among the plurality of flexible meta models; andprocessing, by the process, the observability data obtained within theextensibility platform based on the one or more tenant-specific solutionpackages and the plurality of flexible meta models, wherein theobservability data is associated to the plurality of entities based onexternal references to corresponding globally unique identifiers of theplurality of entities, and wherein the observability data is sourced bya plurality of sources configured to populate, based on a correspondingobserved entity, one or more attribute fields and one or moretenant-specified tag fields according to that corresponding observedentity.

In one embodiment, the method further comprises installing userinterface artifacts within the extensibility platform. In oneembodiment, the method further comprises locating the plurality offlexible meta models according to a manifest within one or more of theone or more tenant-specific solution packages. In one embodiment, therelationships between the plurality of entities comprise how one entityis hierarchically related to another entity. In one embodiment, therelationships between the plurality of entities comprise how one entityinteracts with another entity. In one embodiment, the method furthercomprises processing, from a particular flexible meta model, anextension that either adds one or more attributes or specificobservability data to an existing entity type.

In one embodiment, the observability data comprises metrics, events,spans, and directional associations. In one embodiment, entity typesfurther comprise one or more of: a parent type, a metric type, an eventtype, or an association type. In one embodiment, the method furthercomprises determining a particular source of particular observabilitydata. In one embodiment, determining the particular source is based onone of a telemetry software developer kit name within a payload or anagent type within metadata. In one embodiment, kinds of observabilitydata are defined as one of either a metric, an event, or a trace. In oneembodiment, kinds of observability are statically set within theextensibility platform.

In one embodiment, the method further comprises wherein the plurality offlexible meta models are defined within the one or more tenant-specificsolution packages. In one embodiment, a particular entity is defined asan aggregation of a particular plurality of entities of a particulartype. In one embodiment, the plurality of entities are selected from agroup consisting of: a service, a service instance, a businesstransaction, a host, a representational state transfer endpoint, acontainer, a disk, a thread, a java virtual machine, a topic, adatabase, a router, and a cache. In one embodiment, each flexible metamodel of the plurality of flexible meta models corresponds to aparticular tenant in a multi-tenant architecture for the extensibilityplatform. In one embodiment, the type of each of the plurality ofentities provides validation constraints to be applied to instances ofthat type of entity to ensure attributes of an entity adhere to itsentity kind, and to restrict metric types that are allowed to beassociated with that type of entity. In one embodiment, theobservability data comprises metrics, events, logs, and traces.

According to the embodiments herein, an illustrative tangible,non-transitory, computer-readable medium herein may havecomputer-executable instructions stored thereon that, when executed by aprocessor on a computer, may cause the computer to perform a methodcomprising: providing an extensibility platform for observing aplurality of entities to produce associated observability data, theextensibility platform configured in part by one or more tenant-specificsolution packages; installing a plurality of flexible meta models withinthe extensibility platform, wherein the plurality of flexible metamodels define the plurality of entities, a globally unique identifier ofeach of the plurality of entities, a type of each of the plurality ofentities, relationships between the plurality of entities, kinds ofobservability data, and dependencies among the plurality of flexiblemeta models; and processing the observability data obtained within theextensibility platform based on the one or more tenant-specific solutionpackages and the plurality of flexible meta models, wherein theobservability data is associated to the plurality of entities based onexternal references to corresponding globally unique identifiers of theplurality of entities, and wherein the observability data is sourced bya plurality of sources configured to populate, based on a correspondingobserved entity, one or more attribute fields and one or moretenant-specified tag fields according to that corresponding observedentity.

Further, according to the embodiments herein an illustrative apparatusherein may comprise: one or more network interfaces to communicate witha network; a processor coupled to the network interfaces and configuredto execute one or more processes; and a memory configured to store aprocess that is executable by the processor, the process, when executed,configured to: provide an extensibility platform for observing aplurality of entities to produce associated observability data, theextensibility platform configured in part by one or more tenant-specificsolution packages; install a plurality of flexible meta models withinthe extensibility platform, wherein the plurality of flexible metamodels define the plurality of entities, a globally unique identifier ofeach of the plurality of entities, a type of each of the plurality ofentities, relationships between the plurality of entities, kinds ofobservability data, and dependencies among the plurality of flexiblemeta models; and process the observability data obtained within theextensibility platform based on the one or more tenant-specific solutionpackages and the plurality of flexible meta models, wherein theobservability data is associated to the plurality of entities based onexternal references to corresponding globally unique identifiers of theplurality of entities, and wherein the observability data is sourced bya plurality of sources configured to populate, based on a correspondingobserved entity, one or more attribute fields and one or moretenant-specified tag fields according to that corresponding observedentity.

While there have been shown and described illustrative embodimentsabove, it is to be understood that various other adaptations andmodifications may be made within the scope of the embodiments herein.For example, while certain embodiments are described herein with respectto certain types of applications in particular, such as theobservability intelligence platform, the techniques are not limited assuch and may be used with any computer application, generally, in otherembodiments. For example, as opposed to observability and/or telemetrydata, particularly as related to computer networks and associatedmetrics (e.g., pathways, utilizations, etc.), other applicationplatforms may also utilize the general extensibility platform describedherein, such as for other types of data-based user interfaces, othertypes of data ingestion and aggregation, and so on, may also benefitfrom the extensibility platform described herein.

Moreover, while specific technologies, languages, protocols, andassociated devices have been shown, such as Java, TCP, IP, and so on,other suitable technologies, languages, protocols, and associateddevices may be used in accordance with the techniques described above.In addition, while certain devices are shown, and with certainfunctionality being performed on certain devices, other suitable devicesand process locations may be used, accordingly. That is, the embodimentshave been shown and described herein with relation to specific networkconfigurations (orientations, topologies, protocols, terminology,processing locations, etc.). However, the embodiments in their broadersense are not as limited, and may, in fact, be used with other types ofnetworks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics,these should not be construed as limitations on the scope of anyembodiment or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularembodiments. Certain features that are described in this document in thecontext of separate embodiments can also be implemented in combinationin a single embodiment. Conversely, various features that are describedin the context of a single embodiment can also be implemented inmultiple embodiments separately or in any suitable sub-combination.Further, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure aredescribed in terms of being performed “by a server” or “by a controller”or “by a collection engine”, those skilled in the art will appreciatethat agents of the observability intelligence platform (e.g.,application agents, network agents, language agents, etc.) may beconsidered to be extensions of the server (or controller/engine)operation, and as such, any process step performed “by a server” neednot be limited to local processing on a specific server device, unlessotherwise specifically noted as such. Furthermore, while certain aspectsare described as being performed “by an agent” or by particular types ofagents (e.g., application agents, network agents, endpoint agents,enterprise agents, cloud agents, etc.), the techniques may be generallyapplied to any suitable software/hardware configuration (libraries,modules, etc.) as part of an apparatus, application, or otherwise.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in the present disclosure should not be understoodas requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true intent and scope of theembodiments herein.

What is claimed is:
 1. A method, comprising: providing, by a process, anextensibility platform for observing a plurality of entities to produceassociated observability data, the extensibility platform configured inpart by one or more tenant-specific solution packages; installing, bythe process, a plurality of flexible meta models within theextensibility platform, wherein the plurality of flexible meta modelsdefine the plurality of entities, a globally unique identifier of eachof the plurality of entities, a type of each of the plurality ofentities, relationships between the plurality of entities, kinds ofobservability data, and dependencies among the plurality of flexiblemeta models; and processing, by the process, the observability dataobtained within the extensibility platform based on the one or moretenant-specific solution packages and the plurality of flexible metamodels, wherein the observability data is associated to the plurality ofentities based on external references to corresponding globally uniqueidentifiers of the plurality of entities, and wherein the observabilitydata is sourced by a plurality of sources configured to populate, basedon a corresponding observed entity, one or more attribute fields and oneor more tenant-specified tag fields according to that correspondingobserved entity.
 2. The method as in claim 1, further comprising:installing user interface artifacts within the extensibility platform.3. The method as in claim 1, further comprising: locating the pluralityof flexible meta models according to a manifest within one or more ofthe one or more tenant-specific solution packages.
 4. The method as inclaim 1, wherein the relationships between the plurality of entitiescomprise how one entity is hierarchically related to another entity. 5.The method as in claim 1, wherein the relationships between theplurality of entities comprise how one entity interacts with anotherentity.
 6. The method as in claim 1, further comprising: processing,from a particular flexible meta model, an extension that either adds oneor more attributes or specific observability data to an existing entitytype.
 7. The method as in claim 1, wherein the observability datacomprises metrics, events, spans, and directional associations.
 8. Themethod as in claim 1, wherein entity types further comprise one or moreof: a parent type, a metric type, an event type, or an association type.9. The method as in claim 1, further comprising: determining aparticular source of particular observability data.
 10. The method as inclaim 9, wherein determining the particular source is based on one of atelemetry software developer kit name within a payload or an agent typewithin metadata.
 11. The method as in claim 1, wherein kinds ofobservability data are defined as one of either a metric, an event, or atrace.
 12. The method as in claim 11, wherein kinds of observability arestatically set within the extensibility platform.
 13. The method as inclaim 1, further comprising: wherein the plurality of flexible metamodels are defined within the one or more tenant-specific solutionpackages.
 14. The method as in claim 1, wherein a particular entity isdefined as an aggregation of a particular plurality of entities of aparticular type.
 15. The method as in claim 1, wherein the plurality ofentities are selected from a group consisting of: a service, a serviceinstance, a business transaction, a host, a representational statetransfer endpoint, a container, a disk, a thread, a java virtualmachine, a topic, a database, a router, and a cache.
 16. The method asin claim 1, wherein each flexible meta model of the plurality offlexible meta models corresponds to a particular tenant in amulti-tenant architecture for the extensibility platform.
 17. The methodas in claim 1, wherein the type of each of the plurality of entitiesprovides validation constraints to be applied to instances of that typeof entity to ensure attributes of an entity adhere to its entity kind,and to restrict metric types that are allowed to be associated with thattype of entity.
 18. The method as in claim 1, wherein the observabilitydata comprises metrics, events, logs, and traces.
 19. A tangible,non-transitory, computer-readable medium having computer-executableinstructions stored thereon that, when executed by a processor on acomputer, cause the computer to perform a method comprising: providingan extensibility platform for observing a plurality of entities toproduce associated observability data, the extensibility platformconfigured in part by one or more tenant-specific solution packages;installing a plurality of flexible meta models within the extensibilityplatform, wherein the plurality of flexible meta models define theplurality of entities, a globally unique identifier of each of theplurality of entities, a type of each of the plurality of entities,relationships between the plurality of entities, kinds of observabilitydata, and dependencies among the plurality of flexible meta models; andprocessing the observability data obtained within the extensibilityplatform based on the one or more tenant-specific solution packages andthe plurality of flexible meta models, wherein the observability data isassociated to the plurality of entities based on external references tocorresponding globally unique identifiers of the plurality of entities,and wherein the observability data is sourced by a plurality of sourcesconfigured to populate, based on a corresponding observed entity, one ormore attribute fields and one or more tenant-specified tag fieldsaccording to that corresponding observed entity.
 20. An apparatus,comprising: one or more network interfaces to communicate with anetwork; a processor coupled to the one or more network interfaces andconfigured to execute one or more processes; a memory configured tostore a process that is executable by the processor, the process, whenexecuted, configured to: provide an extensibility platform for observinga plurality of entities to produce associated observability data, theextensibility platform configured in part by one or more tenant-specificsolution packages; install a plurality of flexible meta models withinthe extensibility platform, wherein the plurality of flexible metamodels define the plurality of entities, a globally unique identifier ofeach of the plurality of entities, a type of each of the plurality ofentities, relationships between the plurality of entities, kinds ofobservability data, and dependencies among the plurality of flexiblemeta models; and process the observability data obtained within theextensibility platform based on the one or more tenant-specific solutionpackages and the plurality of flexible meta models, wherein theobservability data is associated to the plurality of entities based onexternal references to corresponding globally unique identifiers of theplurality of entities, and wherein the observability data is sourced bya plurality of sources configured to populate, based on a correspondingobserved entity, one or more attribute fields and one or moretenant-specified tag fields according to that corresponding observedentity.